{"meta":{"query_hash":"97902a144f08","filters":{"topic":"Advanced Image Processing Techniques"},"cohort_total":427,"direct_labels_cover":1,"predictions_cover":427,"exported":427,"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/97902a144f08","api":"https://metacan.xera.ac/api/v1/cohort?topic=Advanced+Image+Processing+Techniques"},"results":[{"id":"W1531553231","doi":"10.1007/978-3-540-69812-8_6","title":"A Simple Scaling Algorithm Based on Areas Pixels","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Pixel; Image scaling; Bilinear interpolation; Computer science; Scaling; Algorithm; Smoothness; Anti-aliasing; Bicubic interpolation; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Aliasing; Image (mathematics); Pattern recognition (psychology); Computer vision; Mathematics; Image processing; Filter (signal processing)","score_opus":0.017021649854461453,"score_gpt":0.26812250506761987,"score_spread":0.2511008552131584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1531553231","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000028234924,0.00030893294,0.99489796,0.00041381657,0.0006062207,0.0003848045,0.000007155776,0.000882709,0.0024955706],"genre_scores_gemma":[0.00872683,0.00003864281,0.9869202,0.0037858372,0.00032953534,0.000017987748,0.0000068855734,0.00006173178,0.00011239444],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99483436,0.00003854666,0.0005752534,0.0021830816,0.0014786657,0.00089011],"domain_scores_gemma":[0.99631333,0.00059932156,0.00038361325,0.0020971587,0.0003740486,0.00023252133],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006454876,0.00074737624,0.00064212846,0.0012370946,0.0004844029,0.00058239436,0.0045263376,0.00035443006,0.000012697361],"category_scores_gemma":[0.00019266515,0.00070444815,0.00017055628,0.0009030019,0.0009401782,0.0008658148,0.001205107,0.0011616193,0.000038770275],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036321658,0.000036640675,0.000008508904,0.000021473494,0.0000030713734,0.00020861434,0.00016296392,0.01807734,0.00007650922,0.0006672906,0.00006949273,0.9806645],"study_design_scores_gemma":[0.00018808333,0.00016898265,0.000018015786,0.0004994186,0.0000032775795,0.000080464444,2.8303635e-8,0.8665597,0.003345043,0.12579373,0.0026302945,0.0007129936],"about_ca_topic_score_codex":0.000010288181,"about_ca_topic_score_gemma":0.0000043649925,"teacher_disagreement_score":0.97995144,"about_ca_system_score_codex":0.0004948418,"about_ca_system_score_gemma":0.0008745327,"threshold_uncertainty_score":0.9995407},"labels":[],"label_agreement":null},{"id":"W1536766384","doi":"10.1007/978-3-540-69812-8_10","title":"A New Super-Resolution Algorithm Based on Areas Pixels and the Sampling Theorem of Papoulis","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Pixel; Wavelet; Noise (video); Algorithm; Artificial intelligence; Similarity (geometry); Resolution (logic); Sampling (signal processing); Set (abstract data type); Image (mathematics); Computer vision","score_opus":0.018229261594582293,"score_gpt":0.258790985889735,"score_spread":0.24056172429515268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1536766384","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000035640521,0.00094606087,0.99611014,0.0011888826,0.00030163673,0.0004025045,0.000004140821,0.00020795879,0.00083508715],"genre_scores_gemma":[0.009637256,0.000120118435,0.9886646,0.0013045317,0.00017616258,0.00000782799,0.000002037675,0.00002659896,0.000060831688],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969456,0.000060089937,0.00046310824,0.0011535826,0.00093203643,0.00044555307],"domain_scores_gemma":[0.99679804,0.0011839594,0.0003440927,0.0013001304,0.0002536226,0.000120174176],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000931952,0.0004559548,0.0005368591,0.0005450485,0.000322543,0.00027277163,0.0025384347,0.00021363827,0.00000471081],"category_scores_gemma":[0.00024592286,0.00032106315,0.00011517943,0.0005427493,0.0017599663,0.00050643325,0.00086438854,0.00069671933,0.0000022350405],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019029974,0.000012991785,0.000005001532,0.00001941603,0.00000399936,0.000012819278,0.00058288465,0.0065872637,0.00008067003,0.010045014,0.00002488668,0.982606],"study_design_scores_gemma":[0.00041410138,0.00012103788,0.000021877138,0.0004917492,0.0000055288488,0.00005430026,9.6398196e-8,0.7436744,0.0015081895,0.25288087,0.00052796636,0.000299856],"about_ca_topic_score_codex":0.000038278635,"about_ca_topic_score_gemma":0.0000067831875,"teacher_disagreement_score":0.9823062,"about_ca_system_score_codex":0.0001491121,"about_ca_system_score_gemma":0.0006201189,"threshold_uncertainty_score":0.9999241},"labels":[],"label_agreement":null},{"id":"W1547931223","doi":"","title":"Fast high dynamic range image deghosting for arbitrary scene motion","year":2012,"lang":"en","type":"article","venue":"Graphics Interface","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Ghosting; Computer vision; Computer science; High dynamic range; Artificial intelligence; High-dynamic-range imaging; Segmentation; Frame (networking); Frame rate; A priori and a posteriori; Dynamic range","score_opus":0.017563943807676233,"score_gpt":0.3034209909849629,"score_spread":0.28585704717728666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1547931223","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011367589,0.000994847,0.9852582,0.0005234868,0.00044599516,0.00029118985,0.00000982941,0.0008944931,0.00021437363],"genre_scores_gemma":[0.49088693,0.00001646185,0.50882626,0.00014436859,0.000036976508,0.000035856436,0.0000032593005,0.000018678345,0.000031214073],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860775,0.000042465243,0.00026149984,0.00038078387,0.00019269553,0.00051482255],"domain_scores_gemma":[0.99889696,0.00012481956,0.00016866504,0.00052380626,0.00018336995,0.00010236683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047558066,0.00020109014,0.00018044989,0.00019620122,0.00020180672,0.00015985637,0.0008417141,0.000092093185,0.0000030575309],"category_scores_gemma":[0.00016052874,0.00021202487,0.00008523296,0.000430295,0.000107757885,0.002066992,0.00030299585,0.0002774787,0.000013663508],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011563208,0.0007975098,0.0037988466,0.0008471772,0.00011765346,0.00001298818,0.0047510574,0.000051151703,0.33642983,0.17766441,0.0019292173,0.47348452],"study_design_scores_gemma":[0.0010460266,0.0002788439,0.004037274,0.00052185985,0.000043635424,0.000108421955,0.000107252235,0.4795989,0.2807467,0.2311129,0.0011684742,0.0012297006],"about_ca_topic_score_codex":0.000009402859,"about_ca_topic_score_gemma":0.000002968213,"teacher_disagreement_score":0.47954774,"about_ca_system_score_codex":0.00006562,"about_ca_system_score_gemma":0.000020824396,"threshold_uncertainty_score":0.8646123},"labels":[],"label_agreement":null},{"id":"W1570786161","doi":"10.1007/978-3-642-17688-3_20","title":"An Edge-Sensing Universal Demosaicing Algorithm","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Computer vision; Demosaicing; Artificial intelligence; Algorithm; Image (mathematics); Image processing","score_opus":0.011281766795614876,"score_gpt":0.2601436021866487,"score_spread":0.24886183539103385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1570786161","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001204538,0.0001752193,0.99541986,0.00032305074,0.0015030595,0.00026577988,0.0000028111072,0.0009335056,0.0013646521],"genre_scores_gemma":[0.004518836,0.000017474022,0.9937751,0.0009819381,0.000527399,0.0000012744881,0.0000042090824,0.000057844776,0.000115881914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957895,0.000035372763,0.00045704766,0.0019881113,0.00091023813,0.0008196983],"domain_scores_gemma":[0.99661267,0.00026526488,0.0003591542,0.00205704,0.00044338498,0.0002624995],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000811092,0.0006312351,0.000531573,0.000998279,0.0005202155,0.0009346694,0.004640687,0.00049942284,0.000007817964],"category_scores_gemma":[0.00009037068,0.0006334447,0.00009866416,0.00068816554,0.001187616,0.0021835745,0.0014993553,0.0018459474,0.000018666486],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013440258,0.000012561491,0.0000030160643,0.000011803879,0.0000029255557,0.00014599091,0.00037198525,0.00069488713,0.0054879473,0.0022245771,0.000003231465,0.99103975],"study_design_scores_gemma":[0.000109694294,0.00011172428,0.0000058163105,0.00026486494,0.0000052444266,0.00016341444,1.4344748e-7,0.7274822,0.017496713,0.2526575,0.0010565275,0.0006461553],"about_ca_topic_score_codex":0.000023522303,"about_ca_topic_score_gemma":0.000047860703,"teacher_disagreement_score":0.9903936,"about_ca_system_score_codex":0.00035581546,"about_ca_system_score_gemma":0.00072942587,"threshold_uncertainty_score":0.9996117},"labels":[],"label_agreement":null},{"id":"W1586298956","doi":"10.1109/icdsp.2015.7251858","title":"Remote sensing image super-resolution: Challenges and approaches","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University","keywords":"Remote sensing; Computer science; Image resolution; Remote sensing application; Interpolation (computer graphics); High resolution; Computer vision; Image (mathematics); Geography","score_opus":0.14228846820436536,"score_gpt":0.2880346430904428,"score_spread":0.14574617488607744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1586298956","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013749154,0.0038822116,0.96408105,0.004904444,0.000044045366,0.00006576136,9.53499e-8,0.00086569425,0.026019229],"genre_scores_gemma":[0.026914226,0.00014953746,0.9725325,0.00013539492,0.00004271393,3.600836e-7,2.9307992e-7,0.000008635146,0.0002163187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913,0.000040164683,0.000110227564,0.0003558633,0.00016465053,0.00019908375],"domain_scores_gemma":[0.9993337,0.000030130488,0.00003351654,0.0003950359,0.000098096585,0.00010955678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032448763,0.00011159053,0.0001128536,0.000060691156,0.00006548023,0.00012963502,0.00028150625,0.00004297495,4.8773023e-7],"category_scores_gemma":[0.00009861747,0.00009747513,0.0000148882755,0.00010249712,0.00010426531,0.0010307697,0.00038664445,0.000090565154,0.00000886163],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020226607,0.0000069074686,8.771979e-7,0.000015994347,0.0000024366298,0.000012805526,0.0010318422,9.739825e-7,0.00088493974,0.012273684,0.0004289865,0.9853385],"study_design_scores_gemma":[0.00019994377,0.00006336428,0.000046736826,0.000033785524,0.000002921836,0.00023219538,0.00029040498,0.810551,0.0076932306,0.1758403,0.0047854963,0.00026065932],"about_ca_topic_score_codex":0.000010780693,"about_ca_topic_score_gemma":0.000005309814,"teacher_disagreement_score":0.98507786,"about_ca_system_score_codex":0.000029659366,"about_ca_system_score_gemma":0.000038073988,"threshold_uncertainty_score":0.39749205},"labels":[],"label_agreement":null},{"id":"W1595923625","doi":"10.1007/978-3-642-03641-5_9","title":"A PDE Approach to Coupled Super-Resolution with Non-parametric Motion","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Regularization (linguistics); Parametric statistics; Affine transformation; Motion (physics); Computer science; Motion estimation; Algorithm; Computer vision; Resolution (logic); Artificial intelligence; Superresolution; Mathematics; Image (mathematics); Mathematical optimization; Geometry","score_opus":0.014953453176964646,"score_gpt":0.24881150795617837,"score_spread":0.23385805477921373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1595923625","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000032107622,0.00025297952,0.9944228,0.00047446677,0.0002672538,0.0008507139,0.0000016497363,0.0006006243,0.0030973877],"genre_scores_gemma":[0.052215118,0.000018306324,0.9459843,0.0013315558,0.00017887879,0.000031354346,0.000005965268,0.00004003111,0.00019448565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99509555,0.000026607991,0.00048845564,0.0021917121,0.0013551714,0.00084250706],"domain_scores_gemma":[0.99709094,0.00017020598,0.0002828403,0.0016977127,0.0005054983,0.00025281377],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00079659227,0.00067099027,0.0006119058,0.0020362667,0.00031421005,0.00077341875,0.0037185145,0.00031550642,0.000001817391],"category_scores_gemma":[0.00013664484,0.0005787686,0.000087601664,0.0026355216,0.00046656013,0.0012778708,0.00091847655,0.0008895381,0.000023494104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001701216,0.000098077166,0.000013680498,0.00004787177,0.0000062765216,0.000037161793,0.00043570923,0.080151215,0.0005940736,0.0038277884,0.000023119304,0.914748],"study_design_scores_gemma":[0.00023108383,0.00043843593,0.00012189008,0.00035340808,0.0000081211465,0.00008791276,9.389213e-8,0.93234456,0.0014919016,0.06384834,0.00032828067,0.0007460035],"about_ca_topic_score_codex":0.000020593718,"about_ca_topic_score_gemma":0.000011200042,"teacher_disagreement_score":0.914002,"about_ca_system_score_codex":0.00064226444,"about_ca_system_score_gemma":0.00048007834,"threshold_uncertainty_score":0.9996664},"labels":[],"label_agreement":null},{"id":"W1603705745","doi":"10.1109/icip.2002.1038154","title":"Error concealment using a diffusion based method","year":2003,"lang":"en","type":"article","venue":"Proceedings - International Conference on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Orientation (vector space); Smoothing; Constraint (computer-aided design); Anisotropic diffusion; Computer science; Smoothness; Mathematical optimization; Block (permutation group theory); Optimization problem; Algorithm; Nonlinear system; Artificial intelligence; Computer vision; Mathematics; Image (mathematics); Geometry","score_opus":0.07309960483373613,"score_gpt":0.3854141608809934,"score_spread":0.3123145560472573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1603705745","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014777156,0.00007024935,0.9580679,0.0012322958,0.0002432486,0.00028479242,0.000003900318,0.00075250026,0.03786742],"genre_scores_gemma":[0.34707794,0.0000072323014,0.6518819,0.00074567524,0.000045946934,0.00005897028,0.0000031391635,0.00003154955,0.00014768715],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99682045,0.00004954636,0.000552454,0.0010575544,0.0009626039,0.0005573885],"domain_scores_gemma":[0.9973735,0.00006197326,0.0005767379,0.00025486344,0.0015493521,0.00018356014],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00079588534,0.00044466436,0.00033466975,0.0004495713,0.00042687476,0.0015784651,0.00154906,0.0001348446,0.000114925424],"category_scores_gemma":[0.00058492686,0.00042844945,0.000089284855,0.0005897801,0.0001496309,0.0029985288,0.0002513421,0.0005004701,0.000021493503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000771167,0.00046114082,0.00054385845,0.00023616175,0.000033252327,0.000030012192,0.0011667577,0.000041873016,0.60789543,0.3253037,0.00025093756,0.06395978],"study_design_scores_gemma":[0.0006012185,0.00008463827,0.00002882368,0.0005078307,0.000013425002,0.000069716094,0.00021117378,0.8565909,0.10199475,0.0379065,0.001471199,0.0005197666],"about_ca_topic_score_codex":0.0000090347,"about_ca_topic_score_gemma":4.0496417e-7,"teacher_disagreement_score":0.8565491,"about_ca_system_score_codex":0.00035600545,"about_ca_system_score_gemma":0.00047617703,"threshold_uncertainty_score":0.9998167},"labels":[],"label_agreement":null},{"id":"W1607611837","doi":"10.1109/icip.2003.1247334","title":"A contour-preserving image interpolation method","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Interpolation (computer graphics); Computer vision; Artificial intelligence; Artifact (error); Stairstep interpolation; Computer science; Image (mathematics); Image scaling; Nearest-neighbor interpolation; Orientation (vector space); Computer graphics (images); Image processing; Multivariate interpolation; Mathematics; Bilinear interpolation; Geometry","score_opus":0.016660896498417215,"score_gpt":0.3369052742982472,"score_spread":0.32024437779982995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1607611837","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003902762,0.000059173035,0.9790755,0.0018087241,0.000048237976,0.00007945365,1.5913271e-7,0.0011575203,0.017732237],"genre_scores_gemma":[0.03148399,0.0000030316814,0.96773565,0.0005378712,0.000021192469,0.000011490763,3.787617e-7,0.0000072754588,0.00019911157],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99927604,0.000027606984,0.00014056395,0.00025859804,0.0001308077,0.00016637737],"domain_scores_gemma":[0.9993244,0.000045905872,0.000060092807,0.00042061514,0.00010516176,0.00004383887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024305862,0.0000861218,0.00008884642,0.000078280886,0.00006941083,0.00020121576,0.0007773499,0.000029813978,0.000020715825],"category_scores_gemma":[0.00014193416,0.0000767145,0.00003029418,0.00023020018,0.00002129503,0.001985445,0.00041570293,0.00009537306,0.00002737885],"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":[0.0000058731507,0.00006539303,0.000052459804,0.000029042407,0.00001000729,0.00002513992,0.0011742171,0.000048117276,0.5697767,0.2641679,0.0010957401,0.1635494],"study_design_scores_gemma":[0.00024975705,0.000039854436,0.00006944838,0.00004679837,0.000001905405,0.00002776383,0.000024705576,0.21851906,0.17050256,0.6095451,0.0007957704,0.00017727829],"about_ca_topic_score_codex":0.000060347622,"about_ca_topic_score_gemma":0.000006326661,"teacher_disagreement_score":0.39927414,"about_ca_system_score_codex":0.000044680997,"about_ca_system_score_gemma":0.000042218213,"threshold_uncertainty_score":0.31283268},"labels":[],"label_agreement":null},{"id":"W1657867270","doi":"10.1109/mwscas.1993.343263","title":"Image zooming by improved interpolation scheme using FFT","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Interpolation (computer graphics); Scheme (mathematics); Fast Fourier transform; Zoom; Computer science; Block (permutation group theory); Enhanced Data Rates for GSM Evolution; Image (mathematics); Artificial intelligence; Computer vision; Algorithm; Computer graphics (images); Mathematics; Engineering; Combinatorics","score_opus":0.021176465830164116,"score_gpt":0.27859998454195845,"score_spread":0.2574235187117943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1657867270","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037723774,0.00014053464,0.9944114,0.00026816354,0.000058059777,0.00008290007,4.2243326e-7,0.0008505874,0.0038106795],"genre_scores_gemma":[0.0070065097,0.0000053508716,0.9921384,0.00029583793,0.000022261063,0.0000048317484,5.945649e-7,0.000012289009,0.00051396864],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991667,0.000016324668,0.00017770845,0.00030598723,0.00011278477,0.00022047492],"domain_scores_gemma":[0.99940926,0.000023347953,0.00009156476,0.00034857212,0.00007935547,0.00004793094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000101110476,0.00011509927,0.00009533763,0.00007419492,0.00012111858,0.00024024179,0.00050063763,0.000040546296,0.000062736966],"category_scores_gemma":[0.00006392505,0.00011096657,0.000029594397,0.0002465443,0.00003956155,0.0019768386,0.0002521358,0.00010878134,0.000023499286],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.4876236e-7,0.000015881262,0.00003278388,0.000005257596,0.0000016587745,9.638459e-7,0.000085224194,3.877544e-9,0.99630374,0.00035031312,0.0008794073,0.0023242938],"study_design_scores_gemma":[0.0000880218,0.000016810769,0.0000036473234,0.000017994425,0.0000012695245,0.000009691987,0.0000068404775,0.9920339,0.005577544,0.0015512184,0.00054802553,0.00014505192],"about_ca_topic_score_codex":0.000016591752,"about_ca_topic_score_gemma":6.2804327e-7,"teacher_disagreement_score":0.9920339,"about_ca_system_score_codex":0.000054697255,"about_ca_system_score_gemma":0.000008502924,"threshold_uncertainty_score":0.45250854},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"split"},{"id":"W1672651452","doi":"10.1109/iscas.2002.1009958","title":"Canny edge based image expansion","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bilinear interpolation; Bicubic interpolation; Canny edge detector; Stairstep interpolation; Interpolation (computer graphics); Image gradient; Artificial intelligence; Deriche edge detector; Computer vision; Mathematics; Enhanced Data Rates for GSM Evolution; Image scaling; Pixel; Demosaicing; Edge detection; Image (mathematics); Computer science; Algorithm; Image processing; Multivariate interpolation; Binary image","score_opus":0.012095847333128983,"score_gpt":0.2691881410219382,"score_spread":0.25709229368880926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1672651452","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000119088756,0.000058830326,0.96419704,0.000447021,0.00007967279,0.000068167545,2.0223784e-7,0.00095450133,0.03407548],"genre_scores_gemma":[0.06389062,0.0000019053829,0.93427366,0.0010107675,0.000007555962,0.000013548379,3.623452e-7,0.000008098411,0.0007934979],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992172,0.000037315094,0.0001113081,0.0002822002,0.00014512506,0.00020685155],"domain_scores_gemma":[0.9992616,0.00003213761,0.00003707887,0.0005109982,0.00009416048,0.00006403678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016512562,0.000096452,0.000080491525,0.00006826305,0.000083950756,0.00011074186,0.00046570116,0.00003031193,0.000047831323],"category_scores_gemma":[0.00012135168,0.00008317454,0.000027220762,0.00028688618,0.000038149734,0.00076818303,0.000067030254,0.00007690202,0.00004998074],"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":[0.0000053017175,0.00023942115,0.00026749945,0.000056343186,0.000005320986,0.00008836513,0.0003023292,0.000012860776,0.61753356,0.25269404,0.029213302,0.09958163],"study_design_scores_gemma":[0.00019054534,0.000042074756,0.000061303916,0.00001870023,0.0000013564745,0.000012402728,0.000007473816,0.0404724,0.89659303,0.040438604,0.021925917,0.00023620522],"about_ca_topic_score_codex":0.000006769708,"about_ca_topic_score_gemma":0.0000017277877,"teacher_disagreement_score":0.27905944,"about_ca_system_score_codex":0.000034408025,"about_ca_system_score_gemma":0.00011669826,"threshold_uncertainty_score":0.33917594},"labels":[],"label_agreement":null},{"id":"W1699515474","doi":"10.1109/icassp.1988.196767","title":"2-D Kalman filtering for the restoration of stochastically blurred images","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Kalman filter; Image restoration; Frame (networking); Computer science; Estimator; Noise (video); Computer vision; Artificial intelligence; Stochastic process; Algorithm; Mathematics; Image (mathematics); Image processing; Statistics","score_opus":0.02427321357412882,"score_gpt":0.2980536159969815,"score_spread":0.2737804024228527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1699515474","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000036641173,0.00009203161,0.9976049,0.00054453337,0.00006283205,0.00018129674,7.214878e-7,0.00018188478,0.0012951514],"genre_scores_gemma":[0.16536789,0.0000029050675,0.83431154,0.00008015341,0.000009051158,0.000037129597,2.3905747e-7,0.0000046296745,0.0001864693],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999479,0.000016511613,0.00013956451,0.00014882581,0.00010201724,0.000114067436],"domain_scores_gemma":[0.9992406,0.00020730798,0.00006606002,0.0003307231,0.00013741199,0.000017894048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023871321,0.00006080309,0.000068662586,0.000032324755,0.00008767567,0.000059720864,0.00040774306,0.000017838227,0.0000037493685],"category_scores_gemma":[0.00041396765,0.00004143772,0.000025719828,0.00013096041,0.00005237599,0.00041681508,0.000060846014,0.000039899543,0.0000010894926],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011950363,0.000057572302,0.000023084225,0.0000559554,0.000011681908,9.3633736e-7,0.00030631202,0.0002569347,0.29594868,0.62860316,0.0031392055,0.071584515],"study_design_scores_gemma":[0.00027166406,0.0001948899,0.00014540405,0.00003954833,0.0000086496275,0.000010922024,0.00003818416,0.20879264,0.627305,0.15823983,0.004751659,0.00020164237],"about_ca_topic_score_codex":0.0000016677631,"about_ca_topic_score_gemma":9.758917e-7,"teacher_disagreement_score":0.47036335,"about_ca_system_score_codex":0.000011962346,"about_ca_system_score_gemma":0.000039621373,"threshold_uncertainty_score":0.16897812},"labels":[],"label_agreement":null},{"id":"W1721184332","doi":"10.1016/j.jvcir.2015.09.004","title":"Blind single-image super resolution based on compressive sensing","year":2015,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Compressed sensing; Image (mathematics); Computer science; Kernel (algebra); Superresolution; Artificial intelligence; Point spread function; Computer vision; Function (biology); Minification; Point (geometry); Domain (mathematical analysis); Image restoration; Algorithm; Image processing; Pattern recognition (psychology); Mathematics","score_opus":0.07912307772709411,"score_gpt":0.39555760814459534,"score_spread":0.31643453041750125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1721184332","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0091699,0.0003241138,0.9856519,0.0026490598,0.000075694465,0.00013154194,6.050105e-7,0.0000839217,0.0019132274],"genre_scores_gemma":[0.44843572,0.00005378693,0.55123526,0.00020977156,0.0000353067,0.0000011490133,0.0000050719864,0.000008841611,0.000015117402],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982113,0.00051329436,0.00048347906,0.00018405633,0.0004703989,0.00013744678],"domain_scores_gemma":[0.99714047,0.00030668383,0.0006465431,0.00053919764,0.0012375294,0.00012960809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008634729,0.00012948681,0.00020077609,0.000301495,0.00016817468,0.0004204172,0.00047607886,0.000055542234,0.0000021298197],"category_scores_gemma":[0.00090770976,0.00011995133,0.000055920536,0.00034560007,0.00016499736,0.0025699788,0.00020051547,0.00031052018,0.0000040107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012022863,0.0015482785,0.0004431618,0.00008313897,0.00006173424,0.000082665865,0.0060421783,0.0011530474,0.7641927,0.0018504357,0.008992909,0.21434745],"study_design_scores_gemma":[0.0022126858,0.0008004466,0.00060211687,0.00029791295,0.000023081939,0.00016192943,0.0006205116,0.90285116,0.0842414,0.0071603507,0.0007966426,0.00023178133],"about_ca_topic_score_codex":0.000011375277,"about_ca_topic_score_gemma":0.0000011351634,"teacher_disagreement_score":0.9016981,"about_ca_system_score_codex":0.00011726795,"about_ca_system_score_gemma":0.00009936679,"threshold_uncertainty_score":0.48914734},"labels":[],"label_agreement":null},{"id":"W1779930287","doi":"10.1007/978-3-540-74260-9_1","title":"A New Image Scaling Algorithm Based on the Sampling Theorem of Papoulis","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Aliasing; Scaling; Computer science; Image scaling; Algorithm; Sampling (signal processing); Image (mathematics); Nyquist–Shannon sampling theorem; Classification of discontinuities; Curvature; Image processing; Mathematics; Artificial intelligence; Computer vision; Filter (signal processing); Geometry; Mathematical analysis","score_opus":0.028100267211453735,"score_gpt":0.2958744394518806,"score_spread":0.2677741722404268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1779930287","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000014486442,0.00023419264,0.99433464,0.00092410436,0.00046636866,0.00037816336,0.000004223625,0.00032431135,0.0033325779],"genre_scores_gemma":[0.0024228909,0.00001222584,0.9948307,0.0023459357,0.00027513423,0.0000046079545,0.0000016721552,0.00004358938,0.00006321408],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959888,0.000041767144,0.0006270993,0.0013564937,0.0013153764,0.00067047164],"domain_scores_gemma":[0.9952044,0.0018029396,0.00052547187,0.0019315225,0.00038519283,0.00015048462],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002057456,0.00056197436,0.00052190054,0.00085496745,0.0002933293,0.00049038813,0.0050000246,0.00026196375,0.000022288039],"category_scores_gemma":[0.00035051352,0.0004077157,0.00017093016,0.0009801816,0.0009816646,0.00057030155,0.001111329,0.0011009712,0.000009791495],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005052071,0.000014399797,0.0000019253876,0.000022245524,0.0000035184542,0.000023846746,0.00024812433,0.003639312,0.0005850523,0.015283961,0.000014268622,0.98015827],"study_design_scores_gemma":[0.00010449239,0.00009011651,0.000005802124,0.00074326916,0.000004401197,0.0000146214825,1.4653477e-7,0.6361079,0.016079022,0.34615102,0.00034352596,0.00035565582],"about_ca_topic_score_codex":0.000019617848,"about_ca_topic_score_gemma":0.0000051180996,"teacher_disagreement_score":0.9798026,"about_ca_system_score_codex":0.00023569731,"about_ca_system_score_gemma":0.0007798309,"threshold_uncertainty_score":0.99983746},"labels":[],"label_agreement":null},{"id":"W1892381122","doi":"10.1109/pacrim.1993.407196","title":"A modified Wiener filter for the restoration of blurred images","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Wiener filter; Filter (signal processing); Image restoration; Fourier transform; Image (mathematics); Operator (biology); Computer science; Computer vision; Artificial intelligence; Algorithm; Mathematics; Image processing; Mathematical analysis; Chemistry","score_opus":0.05170917495759853,"score_gpt":0.28806286652550883,"score_spread":0.2363536915679103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1892381122","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006422453,0.00020156302,0.9947932,0.002489092,0.00004762292,0.00016802628,0.0000011308098,0.00020232098,0.0020328532],"genre_scores_gemma":[0.2771849,0.000012131538,0.7209624,0.00021623018,0.00001847814,0.000053125004,3.182428e-7,0.0000042620704,0.0015481473],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950844,0.000012395146,0.00012547843,0.00014800699,0.00010466771,0.0001009931],"domain_scores_gemma":[0.9992836,0.00011817998,0.00007117712,0.00038884234,0.00012482097,0.000013383047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011649397,0.000058079535,0.000064313586,0.00003565064,0.00007156879,0.00006045788,0.0004987427,0.0000201972,0.000010092821],"category_scores_gemma":[0.00009080636,0.000036240453,0.0000302873,0.00014602861,0.000050881164,0.00062499725,0.00008070342,0.000034616914,0.0000027781778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022875243,0.00020273593,0.000036417547,0.00007675858,0.000025752248,0.0000017881077,0.0016336691,0.0006963616,0.1606773,0.13464187,0.13827018,0.56371427],"study_design_scores_gemma":[0.0001411147,0.00005030556,0.000045186596,0.000009606975,0.000003322685,0.0000020652649,0.0000065609197,0.86398774,0.10600651,0.026803061,0.0028657918,0.000078728],"about_ca_topic_score_codex":0.000004178472,"about_ca_topic_score_gemma":0.0000010529119,"teacher_disagreement_score":0.8632914,"about_ca_system_score_codex":0.000009566278,"about_ca_system_score_gemma":0.000007896314,"threshold_uncertainty_score":0.14778428},"labels":[],"label_agreement":null},{"id":"W1946766895","doi":"10.1109/cvpr.2015.7299153","title":"Data-driven sparsity-based restoration of JPEG-compressed images in dual transform-pixel domain","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; ASTER","funders":"","keywords":"Discrete cosine transform; JPEG; Computer science; Quantization (signal processing); Artificial intelligence; Computer vision; Pixel; Transform coding; Residual; JPEG 2000; Image restoration; Compression artifact; Data compression; Pattern recognition (psychology); Image (mathematics); Image compression; Algorithm; Image processing","score_opus":0.06958908130091236,"score_gpt":0.3210086867541357,"score_spread":0.25141960545322334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1946766895","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004405079,0.000058001577,0.9918066,0.001379675,0.000051597246,0.00019958167,0.000018615538,0.0003524248,0.0017284155],"genre_scores_gemma":[0.29041627,0.0000020763405,0.70938873,0.00010777713,0.0000117410455,0.000007601294,0.000025793406,0.0000067492847,0.000033288008],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864423,0.000088140325,0.00031461671,0.00039881904,0.0003504198,0.00020375349],"domain_scores_gemma":[0.99862945,0.0000611014,0.00011759951,0.0009549302,0.00016056244,0.000076357916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005603469,0.00012953115,0.00020180791,0.00019530703,0.000035485806,0.00007722348,0.0010694991,0.00005544181,0.0000028722286],"category_scores_gemma":[0.00008106237,0.00012111765,0.000021083004,0.0004454015,0.00009947097,0.0021645576,0.00024950778,0.00012816454,0.000005509329],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005020074,0.0021011967,0.0045766216,0.00047023647,0.000042265387,0.0002750736,0.005554253,0.0091752,0.8176749,0.048461754,0.04150807,0.06965843],"study_design_scores_gemma":[0.002088603,0.00027121033,0.0006328482,0.000116596864,0.0000081725675,0.000013608047,0.00010299664,0.65698886,0.25350296,0.0793253,0.00646169,0.00048713863],"about_ca_topic_score_codex":0.000052193245,"about_ca_topic_score_gemma":0.00009089892,"teacher_disagreement_score":0.6478137,"about_ca_system_score_codex":0.000058339,"about_ca_system_score_gemma":0.00023395718,"threshold_uncertainty_score":0.49390343},"labels":[],"label_agreement":null},{"id":"W1956263714","doi":"10.1109/icip.2001.958269","title":"A new edge-directed image expansion scheme","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Sharpening; Interpolation (computer graphics); Enhanced Data Rates for GSM Evolution; Computer science; Image (mathematics); Fidelity; Scheme (mathematics); Computer vision; Image gradient; Image scaling; Artificial intelligence; Stairstep interpolation; Algorithm; Image processing; Edge detection; Bilinear interpolation; Mathematics; Multivariate interpolation; Telecommunications","score_opus":0.019099036487903762,"score_gpt":0.2630388001288846,"score_spread":0.24393976364098083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1956263714","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007679124,0.00028217558,0.96596736,0.0014154803,0.00007252534,0.00007770482,1.12249964e-7,0.004602197,0.027505653],"genre_scores_gemma":[0.004335799,0.000024398867,0.9862571,0.00047036825,0.000046010155,0.0000072187713,3.2920235e-7,0.000011656067,0.008847136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990522,0.000017219985,0.00014574628,0.00035504263,0.00018713657,0.00024263636],"domain_scores_gemma":[0.9991508,0.000027345895,0.00004960238,0.00056854956,0.00009384663,0.00010982663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000061112085,0.00012338706,0.000108916545,0.00008710137,0.000079835925,0.0001729608,0.00074632396,0.000042856456,0.000308074],"category_scores_gemma":[0.00008827871,0.000108727865,0.000034250133,0.00048752967,0.000026886564,0.0013746199,0.00028203576,0.00011169678,0.00032693482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017612213,0.00006724277,0.000027193037,0.0000120078,0.000004345507,0.0000280585,0.0003629245,2.5828632e-7,0.27469546,0.009406914,0.20896597,0.5064279],"study_design_scores_gemma":[0.00042511657,0.00008602253,0.00010878074,0.00006228254,0.0000029237237,0.000058496735,0.000008176019,0.60887533,0.31850046,0.04136872,0.029977817,0.0005259078],"about_ca_topic_score_codex":0.0000135025075,"about_ca_topic_score_gemma":0.0000012029466,"teacher_disagreement_score":0.60887504,"about_ca_system_score_codex":0.000029674185,"about_ca_system_score_gemma":0.000025486253,"threshold_uncertainty_score":0.44337937},"labels":[],"label_agreement":null},{"id":"W1963888222","doi":"10.1117/1.3431711","title":"Computation-aware algorithm selection approach for interlaced-to-progressive conversion","year":2010,"lang":"en","type":"article","venue":"Optical Engineering","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Algorithm; Computation; Interpolation (computer graphics); Covariance; CAD; Mean squared error; Selection (genetic algorithm); Artificial intelligence; Image (mathematics); Mathematics","score_opus":0.006331781784779239,"score_gpt":0.2564097729577699,"score_spread":0.2500779911729907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963888222","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045580926,0.000009502594,0.9980668,0.00012546507,0.00022203613,0.0002655319,0.0000011917153,0.0007811247,0.000072571725],"genre_scores_gemma":[0.11584619,2.3453063e-7,0.8838979,0.00004902287,0.00007796376,0.00009033581,0.0000054650513,0.000016773314,0.000016142685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991722,0.0000032141788,0.00013399182,0.00031998003,0.00013071786,0.00023987204],"domain_scores_gemma":[0.999465,0.00007864778,0.000031391355,0.00013437918,0.00017182203,0.0001187507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000114715906,0.00012628862,0.000119734796,0.00011185954,0.00007451325,0.00013267658,0.00032824356,0.00007276913,0.000001628877],"category_scores_gemma":[0.00013207251,0.00012841803,0.00003845334,0.00032449368,0.00001930687,0.00040526091,0.00013365687,0.0002312603,0.0000048388115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020677651,0.00019593303,0.000025756754,0.0002924037,0.000041970387,0.00001076575,0.0004438353,0.043742083,0.25959042,0.029159073,0.0006991814,0.6657779],"study_design_scores_gemma":[0.00012987397,0.00007200753,0.000016923887,0.000016958089,0.0000031498741,0.000023481583,0.0000046686055,0.9557003,0.04329498,0.0002974778,0.00028615934,0.00015406321],"about_ca_topic_score_codex":6.954766e-7,"about_ca_topic_score_gemma":6.145824e-8,"teacher_disagreement_score":0.91195816,"about_ca_system_score_codex":0.000039900642,"about_ca_system_score_gemma":0.000022469278,"threshold_uncertainty_score":0.52367353},"labels":[],"label_agreement":null},{"id":"W1966517620","doi":"10.1109/icip.2012.6467001","title":"Image resolution up-conversion via maximum a posteriori interpolator sequence estimation and Viterbi algorithm","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Maximum a posteriori estimation; Viterbi algorithm; Algorithm; Interpolation (computer graphics); Computer science; Sequence (biology); Soft output Viterbi algorithm; Trellis (graph); A priori and a posteriori; Pixel; Image scaling; Image (mathematics); Artificial intelligence; Mathematics; Image processing; Maximum likelihood; Decoding methods","score_opus":0.014696457989138892,"score_gpt":0.28432002580057686,"score_spread":0.269623567811438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966517620","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021975217,0.00016988066,0.9958598,0.00038130974,0.00027845398,0.00015064207,0.0000019536758,0.0006973668,0.00026306923],"genre_scores_gemma":[0.118678965,0.000010910289,0.8808008,0.0002977639,0.000034736873,0.000011597784,0.0000032035716,0.000010594839,0.00015145162],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989286,0.00004339099,0.0001981422,0.0003171847,0.00018730712,0.0003253936],"domain_scores_gemma":[0.9992641,0.000031341686,0.000112916576,0.00036494117,0.000098866185,0.00012783249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027826245,0.00015791401,0.00012415487,0.00012364771,0.00013551411,0.00019057632,0.00035556647,0.00006625983,0.00001218318],"category_scores_gemma":[0.000042050273,0.00014620119,0.000025790236,0.00022637608,0.00009388392,0.005131943,0.00044900033,0.00012154634,0.000043822965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012410701,0.000042983625,0.0003559564,0.00008238736,0.0000054681855,0.000005664011,0.0011775729,2.5100346e-7,0.23814769,0.00076168834,0.000363359,0.7590446],"study_design_scores_gemma":[0.00016847927,0.000076253025,0.00052734406,0.000057909034,0.000005711262,0.00019165661,0.000014935864,0.9441403,0.045243897,0.008865684,0.00047145155,0.00023634417],"about_ca_topic_score_codex":0.000023502707,"about_ca_topic_score_gemma":3.5922784e-7,"teacher_disagreement_score":0.9441401,"about_ca_system_score_codex":0.000112451366,"about_ca_system_score_gemma":0.000021757745,"threshold_uncertainty_score":0.5961911},"labels":[],"label_agreement":null},{"id":"W1972127941","doi":"10.1117/1.jei.21.1.013011","title":"Combining distributed video coding with super-resolution to achieve H.264/AVC performance","year":2012,"lang":"en","type":"article","venue":"Journal of Electronic Imaging","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Innovation, Science and Economic Development Canada; Communications Research Centre Canada","funders":"McGill University","keywords":"Computer science; Codec; Upsampling; Encoder; Decoding methods; Algorithm; Context-adaptive binary arithmetic coding; Real-time computing; Data compression; Computer vision; Telecommunications","score_opus":0.007244783625771539,"score_gpt":0.24870169972722436,"score_spread":0.2414569161014528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972127941","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040673904,0.0010079799,0.95575696,0.0020297822,0.00013764757,0.000100573896,4.869259e-7,0.0001395308,0.00015310508],"genre_scores_gemma":[0.7987204,0.00003863043,0.2007966,0.00028949406,0.00011789825,0.000004874782,8.764726e-7,0.000017354738,0.0000138576725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800265,0.000059409846,0.00039988902,0.00018836303,0.00042408612,0.00092559814],"domain_scores_gemma":[0.998774,0.00007155796,0.00037627676,0.00028027975,0.00031721656,0.00018070197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010502174,0.0001914643,0.00025367102,0.00024937926,0.00025238737,0.00018449876,0.00077350996,0.000026493692,0.0000025735542],"category_scores_gemma":[0.00009315146,0.00015951993,0.00005636526,0.0006195938,0.00004635066,0.003909066,0.00017645296,0.000601242,0.0000050856406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014508022,0.0011558578,0.09951327,0.00035069528,0.00037890242,0.00021540538,0.011002367,0.007827327,0.35009167,0.054434303,0.007846258,0.46573317],"study_design_scores_gemma":[0.0045188144,0.0045568678,0.012234751,0.0028898444,0.00019367073,0.012995173,0.0004922664,0.7044124,0.20725767,0.010524237,0.03740698,0.0025173272],"about_ca_topic_score_codex":0.000003319147,"about_ca_topic_score_gemma":8.358519e-7,"teacher_disagreement_score":0.7580465,"about_ca_system_score_codex":0.00055401574,"about_ca_system_score_gemma":0.0002599359,"threshold_uncertainty_score":0.6505034},"labels":[],"label_agreement":null},{"id":"W1972820105","doi":"10.1117/12.2082861","title":"Restoration of block-transform compressed images via homotopic regularized sparse reconstruction","year":2015,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Block (permutation group theory); Lossy compression; Compressed sensing; Computer science; Iterative reconstruction; Algorithm; Artificial intelligence; Transform coding; Image restoration; Computer vision; Quantization (signal processing); Sparse matrix; Discrete cosine transform; Image compression; Data compression; Lapped transform; Mathematics; Image (mathematics); Image processing","score_opus":0.01524093251151144,"score_gpt":0.2410187561697188,"score_spread":0.22577782365820737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972820105","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9428124,0.00015621442,0.05173872,0.0027465145,0.0003126183,0.00066890696,0.00001601812,0.00035298994,0.0011956344],"genre_scores_gemma":[0.21415843,0.00004939831,0.7854226,0.000028935548,0.0001232029,0.00009351918,0.0000036140343,0.00003169306,0.00008859176],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976999,3.82134e-8,0.00080019387,0.00042483455,0.00076331955,0.00031167213],"domain_scores_gemma":[0.9959961,0.000083617546,0.00063981133,0.00011971036,0.0030518686,0.00010886052],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007532871,0.00028532554,0.00044210514,0.00015725316,0.00007378622,0.00013611218,0.001474006,0.00017895653,0.0000013657233],"category_scores_gemma":[0.00048583144,0.00025263825,0.00035499292,0.0004847913,0.0003254563,0.0018332407,0.00021685046,0.00028002806,5.672595e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066061824,0.000104860344,0.000108813896,0.0003564233,0.00012425904,9.91005e-8,0.0003151726,0.000073830306,0.82922983,0.16553955,0.0010215932,0.0030595192],"study_design_scores_gemma":[0.001170393,0.00033299794,0.00013011192,0.00037316166,0.00007028772,0.000051114042,0.00039959792,0.18392071,0.7829835,0.029540114,0.00070609903,0.0003218943],"about_ca_topic_score_codex":0.000012148174,"about_ca_topic_score_gemma":1.3944195e-7,"teacher_disagreement_score":0.7336839,"about_ca_system_score_codex":0.00017317811,"about_ca_system_score_gemma":0.000073442265,"threshold_uncertainty_score":0.9999926},"labels":[],"label_agreement":null},{"id":"W1976293026","doi":"10.1117/12.871385","title":"Achieving H.264/AVC performance using distributed video coding combined with super-resolution","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Codec; Upsampling; Encoder; Decoding methods; Coding (social sciences); Bilinear interpolation; Coding tree unit; Algorithm; Real-time computing; Computer vision; Computer hardware","score_opus":0.01122948550548168,"score_gpt":0.23451992172482913,"score_spread":0.22329043621934747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976293026","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9529649,0.000036900557,0.043962665,0.0014985297,0.00022486804,0.0005264683,0.000019723991,0.00046202142,0.00030389283],"genre_scores_gemma":[0.46190754,0.000020211688,0.5377985,0.000037113394,0.00011111425,0.00006654501,0.0000048111756,0.00003716812,0.000016973996],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973475,2.4193247e-8,0.0006706148,0.0005815713,0.00083581044,0.0005644836],"domain_scores_gemma":[0.9969328,0.00013678816,0.0005208202,0.00014125899,0.0021411506,0.00012718108],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007683546,0.0004021226,0.00043957948,0.00014675822,0.00026163127,0.00033569592,0.0020656402,0.0002000725,0.0000027054916],"category_scores_gemma":[0.0005801229,0.00032922433,0.00031217159,0.00068918796,0.00035564307,0.0024717608,0.00047179643,0.0006635983,6.661384e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000828217,0.00008922337,0.00087828137,0.0003607745,0.00010948158,1.687669e-7,0.00014366071,0.00014426745,0.75695044,0.24042189,0.00024790017,0.000571068],"study_design_scores_gemma":[0.0010338224,0.00046794655,0.00090449845,0.0006537437,0.00008687147,0.00007863862,0.0002564248,0.70873165,0.28501853,0.0013661529,0.0008473326,0.00055440154],"about_ca_topic_score_codex":0.000006386376,"about_ca_topic_score_gemma":2.1185946e-7,"teacher_disagreement_score":0.70858735,"about_ca_system_score_codex":0.00019082057,"about_ca_system_score_gemma":0.00007466404,"threshold_uncertainty_score":0.99991596},"labels":[],"label_agreement":null},{"id":"W1979027189","doi":"10.1109/tce.2013.6626245","title":"Video super resolution using contourlet transform and bilateral total variation filter","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Waterloo","funders":"","keywords":"Contourlet; Computer science; Deblurring; Computer vision; Artificial intelligence; Filter (signal processing); Frame (networking); Resolution (logic); Image processing; Wavelet transform; Image (mathematics); Image restoration; Wavelet; Telecommunications","score_opus":0.012734103651590898,"score_gpt":0.24530857712236603,"score_spread":0.23257447347077514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979027189","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018530121,0.00044732777,0.97924,0.00074950454,0.00016761321,0.0003892923,0.000005594691,0.00040320147,0.00006736205],"genre_scores_gemma":[0.78239,0.00011608677,0.21699332,0.00023829428,0.000015724501,0.00007573659,0.0000013309602,0.000022049217,0.00014744875],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985413,0.00006121683,0.00027883193,0.00043987756,0.00021033747,0.00046841468],"domain_scores_gemma":[0.99927425,0.0000749091,0.000063069725,0.00034628375,0.00014931665,0.00009218532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014411603,0.00021975598,0.00018200476,0.0001851661,0.00031008912,0.00025995294,0.00023030397,0.00012409476,0.000046803805],"category_scores_gemma":[0.0000048852976,0.00021710467,0.0000635261,0.00028239522,0.0000826215,0.0019157942,0.0000031332747,0.000371834,0.000021173719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091029,0.00036803386,0.000033586355,0.0000742358,0.00017075174,0.000007834007,0.0018464087,0.0029115432,0.5035775,0.0023634802,0.00029883173,0.4882568],"study_design_scores_gemma":[0.00072944874,0.00025522374,0.00015262465,0.00004870287,0.000049179103,0.000175326,0.000009237184,0.88199973,0.10495321,0.010416227,0.00074063067,0.0004704704],"about_ca_topic_score_codex":0.00012992686,"about_ca_topic_score_gemma":0.000032093903,"teacher_disagreement_score":0.87908816,"about_ca_system_score_codex":0.00021129879,"about_ca_system_score_gemma":0.0001389039,"threshold_uncertainty_score":0.8853271},"labels":[],"label_agreement":null},{"id":"W1988374871","doi":"10.1111/j.1467-8659.2012.03211.x","title":"Registration Based Non‐uniform Motion Deblurring","year":2012,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Deblurring; Motion blur; Computer vision; Artificial intelligence; Perspective (graphical); Computer science; Motion (physics); Point spread function; Motion interpolation; Shake; Image restoration; Motion estimation; Image (mathematics); Mathematics; Image processing; Block-matching algorithm; Object (grammar); Video tracking","score_opus":0.017780350645559325,"score_gpt":0.2634282026037313,"score_spread":0.24564785195817196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988374871","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00068936974,0.00009077202,0.9963738,0.0007753039,0.0005462616,0.00016526824,7.027446e-7,0.0008646248,0.00049392885],"genre_scores_gemma":[0.4427478,0.000004111098,0.5562902,0.00081681367,0.000099681216,0.000014506811,0.0000054844495,0.000011686323,0.000009698078],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856555,0.000030369634,0.00026148194,0.00032586217,0.00028596862,0.00053079077],"domain_scores_gemma":[0.9988185,0.000046428995,0.00016808593,0.0006914136,0.00013850605,0.00013702255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039137053,0.00019176278,0.00014372326,0.00026306312,0.00025457665,0.00021477928,0.0007879881,0.00009520277,0.0000014847403],"category_scores_gemma":[0.000017896487,0.00019360367,0.000087310495,0.0005969508,0.000063595944,0.0024194785,0.0002699325,0.0002134495,0.000013327587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010643938,0.00040090398,0.021646818,0.00014256466,0.000025702144,0.0000114588,0.00052833103,0.0003588308,0.002087204,0.69003564,0.0061783986,0.2785735],"study_design_scores_gemma":[0.00018613048,0.0000666901,0.0021997055,0.000055760058,0.0000044038875,0.00001978266,0.0000032808775,0.96337944,0.0061549935,0.024226088,0.0034324117,0.00027129747],"about_ca_topic_score_codex":0.000006237829,"about_ca_topic_score_gemma":0.000002999815,"teacher_disagreement_score":0.9630206,"about_ca_system_score_codex":0.00005213618,"about_ca_system_score_gemma":0.000038155707,"threshold_uncertainty_score":0.78949285},"labels":[],"label_agreement":null},{"id":"W1990566152","doi":"10.1109/tip.2007.891794","title":"A New Orientation-Adaptive Interpolation Method","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":179,"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":"Bilinear interpolation; Interpolation (computer graphics); Stairstep interpolation; Orientation (vector space); Computer vision; Nearest-neighbor interpolation; Kernel (algebra); Artificial intelligence; Bicubic interpolation; Curvature; Trilinear interpolation; Multivariate interpolation; Pixel; Mathematics; Computer science; Geometry; Image (mathematics)","score_opus":0.01958786123059618,"score_gpt":0.3384977403219513,"score_spread":0.3189098790913551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990566152","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022723443,0.000075831056,0.9962934,0.00026765413,0.0002795029,0.00020008419,0.0000014353476,0.001160322,0.0016990207],"genre_scores_gemma":[0.16114521,0.0000035374906,0.83811086,0.00028061718,0.000051633888,0.000017178416,5.8488257e-7,0.000026917704,0.00036344206],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99821866,0.000047193524,0.00039301006,0.0005846496,0.0003697564,0.00038675254],"domain_scores_gemma":[0.99881405,0.00015436456,0.00020753531,0.00036842257,0.00029618782,0.00015942098],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000540639,0.00023946672,0.00018050963,0.00042926712,0.00041254642,0.00037941174,0.0005470795,0.000086639004,0.00002465892],"category_scores_gemma":[0.00002121473,0.00024620237,0.00007958901,0.0011214777,0.000055867466,0.0034410441,0.0000056599533,0.00037883638,0.000034377405],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045641562,0.000062160776,0.0000016349138,0.000019986664,0.000008321266,0.000010090545,0.0014744861,0.000101705846,0.059700996,0.0003887262,0.0000745718,0.93811166],"study_design_scores_gemma":[0.00046473122,0.0001595541,0.000029622595,0.00018145175,0.000023782983,0.000073172705,0.00022781441,0.36204603,0.6167886,0.019335989,0.00024272283,0.00042650534],"about_ca_topic_score_codex":0.000028698401,"about_ca_topic_score_gemma":0.000013686111,"teacher_disagreement_score":0.9376852,"about_ca_system_score_codex":0.00015508303,"about_ca_system_score_gemma":0.00022897053,"threshold_uncertainty_score":0.99999905},"labels":[],"label_agreement":null},{"id":"W1992331286","doi":"10.1109/icip.2010.5650774","title":"Improvement of H.264 SVC by model-based adaptive resolution upconversion","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Scalable Video Coding; Scalability; Decoding methods; Codec; Algorithm; Encoding (memory); Sampling (signal processing); Macro; Real-time computing; Filter (signal processing); Computer hardware; Motion compensation; Computer vision; Artificial intelligence","score_opus":0.008555301770508433,"score_gpt":0.24485026615401378,"score_spread":0.23629496438350536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992331286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022036366,0.000016882077,0.9956397,0.0003362329,0.00006941129,0.00012346194,0.0000024560477,0.0003385339,0.0012697191],"genre_scores_gemma":[0.40469706,9.727432e-7,0.5949099,0.00017845145,0.000004346171,0.000012917525,0.0000010356621,0.000004028074,0.00019129984],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991914,0.000008292168,0.00016388006,0.00027314472,0.00020435985,0.00015889443],"domain_scores_gemma":[0.9992127,0.000021905298,0.00011707276,0.0004364635,0.00016378291,0.000048098365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017061741,0.00009346831,0.00009652362,0.000065859,0.000059312235,0.000024345514,0.00052912504,0.000056989342,0.0000116182855],"category_scores_gemma":[0.000024800089,0.0000834502,0.000032695403,0.00015468748,0.00007314693,0.00053977215,0.00017173833,0.0001400771,0.0000040542127],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012569629,0.0000755061,0.000014081152,0.000011409878,0.0000021655635,3.89264e-7,0.000041925978,0.00018386939,0.9412923,0.010399865,0.0040113693,0.043954592],"study_design_scores_gemma":[0.00010213416,0.00007783794,0.0000021733322,0.0000060927323,0.0000010850206,2.7037635e-7,0.0000023615657,0.5524667,0.43979293,0.007321976,0.00016800805,0.000058422153],"about_ca_topic_score_codex":0.000032155596,"about_ca_topic_score_gemma":0.0000043309647,"teacher_disagreement_score":0.55228287,"about_ca_system_score_codex":0.00002940279,"about_ca_system_score_gemma":0.00008143626,"threshold_uncertainty_score":0.34030005},"labels":[],"label_agreement":null},{"id":"W1993585959","doi":"10.1109/mmsp.2010.5662061","title":"Video super-resolution for dual-mode digital cameras via scene-matched learning","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Subpixel rendering; Interpolation (computer graphics); Image resolution; Pixel; Image (mathematics)","score_opus":0.009388512861112652,"score_gpt":0.2823785736545202,"score_spread":0.27299006079340754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993585959","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005980188,0.000021749356,0.98989064,0.00083997287,0.0001956113,0.00020679836,0.0000022317656,0.0016285314,0.0012342577],"genre_scores_gemma":[0.45499906,0.0000012328135,0.5441589,0.00010641594,0.000056555175,0.000033803182,0.0000044361154,0.0000139263975,0.00062567525],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875116,0.0000135413775,0.00021594376,0.00045057558,0.00019882254,0.00036996417],"domain_scores_gemma":[0.99907655,0.00012127997,0.000079351325,0.0004252337,0.00020474005,0.00009286052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001841546,0.00016560768,0.00015990999,0.00009265849,0.00024180612,0.0004457984,0.00052875874,0.00009011726,0.000013442077],"category_scores_gemma":[0.00033697268,0.00015335517,0.00006702017,0.00021278975,0.00008253558,0.00232407,0.00026681353,0.0003007628,0.000032819575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015860314,0.00009156546,0.0008066773,0.000034359073,0.0000110378405,0.0000055464075,0.0005476952,0.00013430839,0.64936095,0.012634845,0.0012254465,0.3351317],"study_design_scores_gemma":[0.00030166208,0.00011984897,0.00012961717,0.000018573688,0.0000042711326,0.000047535304,0.000022309916,0.82378393,0.11005162,0.053413026,0.011723389,0.000384242],"about_ca_topic_score_codex":0.000024132734,"about_ca_topic_score_gemma":0.000020118001,"teacher_disagreement_score":0.8236496,"about_ca_system_score_codex":0.000034159202,"about_ca_system_score_gemma":0.00006316854,"threshold_uncertainty_score":0.62536424},"labels":[],"label_agreement":null},{"id":"W1997803708","doi":"10.1109/icip.2011.6115689","title":"Two-step super-resolution technique using bounded total variation and bisquare M-estimator under local illumination changes","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Upsampling; Artificial intelligence; Estimator; Computer science; Superresolution; Computer vision; Image resolution; Focus (optics); Iterative reconstruction; Image (mathematics); Bounded function; Image registration; Resolution (logic); Frame (networking); Algorithm; Mathematics; Optics; Statistics","score_opus":0.03578453784126192,"score_gpt":0.2820196212211578,"score_spread":0.24623508337989589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997803708","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035165846,0.00006925499,0.9942103,0.00022649733,0.00008133829,0.0003382,0.0000011700654,0.000955754,0.00060090463],"genre_scores_gemma":[0.4306553,0.0000022028503,0.56919134,0.00006025096,0.000016312832,0.00003351576,0.0000018033444,0.0000104412975,0.000028833965],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884886,0.000060911316,0.00018900665,0.00042887146,0.00021675727,0.00025558256],"domain_scores_gemma":[0.99927264,0.00002914576,0.00010992699,0.00032072188,0.00019849947,0.0000690715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002939436,0.00017522613,0.00013991352,0.00021605603,0.0002373213,0.00013673051,0.0002587003,0.00010626933,0.000012763366],"category_scores_gemma":[0.000039779334,0.00017002979,0.000021006383,0.00035151775,0.00014923795,0.0015067139,0.00024556494,0.00011571188,0.0000031306774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046887428,0.00032242207,0.0002779375,0.00017920787,0.000032124117,0.00003205165,0.003923379,0.0001494163,0.3654347,0.516971,0.000106589774,0.11252431],"study_design_scores_gemma":[0.00022726128,0.00013376564,0.001276834,0.000067155874,0.000011390868,0.00021055754,0.000112517904,0.8685364,0.0810259,0.048063144,0.00002294589,0.00031211114],"about_ca_topic_score_codex":0.00030154252,"about_ca_topic_score_gemma":0.000037749836,"teacher_disagreement_score":0.868387,"about_ca_system_score_codex":0.00017028801,"about_ca_system_score_gemma":0.00006821691,"threshold_uncertainty_score":0.69336134},"labels":[],"label_agreement":null},{"id":"W1999201748","doi":"10.5589/m07-052","title":"A self-adaptive motion estimation algorithm for superresolution reconstruction of multiframe SPOT panchromatic images","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Subpixel rendering; Panchromatic film; Artificial intelligence; Computer vision; Computer science; Optical transfer function; Motion estimation; Image resolution; Interpolation (computer graphics); Iterative reconstruction; Point spread function; Pixel; Image (mathematics); Optics; Physics","score_opus":0.014454445821630756,"score_gpt":0.25661113352857085,"score_spread":0.2421566877069401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999201748","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061454717,0.00020882252,0.9928588,0.00012297789,0.00039338515,0.00017866958,0.000002652168,0.000054029624,0.000035163368],"genre_scores_gemma":[0.15237199,0.000006233175,0.8475088,0.000020596914,0.00007670258,2.9554162e-8,9.2242993e-7,0.000011192776,0.0000035272694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988422,0.000042451302,0.0005136799,0.0001559226,0.00016452342,0.0002812254],"domain_scores_gemma":[0.9981553,0.000110534835,0.0005839419,0.00016807918,0.00078317063,0.00019895019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008730391,0.00012017384,0.00021679941,0.00053943106,0.00015881105,0.00007239176,0.00018630372,0.00008452324,4.4071047e-7],"category_scores_gemma":[0.00031611772,0.00012750021,0.00008431473,0.00032630295,0.00008537128,0.0010501209,0.000010401957,0.00017161126,4.4766819e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003998606,0.0000032575401,0.0000030437955,0.000027405174,0.000012114064,0.000023567402,0.00048154342,0.000043830987,0.0040422343,0.000041565414,0.000012999027,0.99530447],"study_design_scores_gemma":[0.0002373496,0.00011821359,0.00017027068,0.00037499404,0.000021919164,0.0013859551,0.00011510004,0.93401134,0.05348691,0.009888435,0.00007396095,0.00011554857],"about_ca_topic_score_codex":0.0005417879,"about_ca_topic_score_gemma":0.0005217964,"teacher_disagreement_score":0.9951889,"about_ca_system_score_codex":0.00045034726,"about_ca_system_score_gemma":0.00045803396,"threshold_uncertainty_score":0.5199308},"labels":[],"label_agreement":null},{"id":"W2001010260","doi":"10.1117/1.jbo.19.5.056002","title":"Comparison of super-resolution algorithms applied to retinal images","year":2014,"lang":"en","type":"article","venue":"Journal of Biomedical Optics","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"University of Waterloo","keywords":"Computer science; Image resolution; Interpolation (computer graphics); Resolution (logic); Regularization (linguistics); Algorithm; Computer vision; Image processing; Artificial intelligence; Optics; Image (mathematics); Physics","score_opus":0.019505738789086558,"score_gpt":0.3357951354370884,"score_spread":0.3162893966480018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001010260","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023777972,0.000090719164,0.99456096,0.0022794097,0.00023269495,0.000054548418,0.0000011030342,0.000049061055,0.00035371573],"genre_scores_gemma":[0.2720356,0.000009515805,0.7276447,0.00013412953,0.00015846954,9.353086e-7,4.8907555e-7,0.000006161839,0.000009991802],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809,0.00004549516,0.0007287031,0.00015454335,0.00075928826,0.00022194946],"domain_scores_gemma":[0.99851036,0.00014568478,0.00046566644,0.00025736337,0.00037671195,0.0002442299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092192396,0.000113813134,0.00037889846,0.00025593027,0.00005232837,0.00005453111,0.0009518847,0.00008402131,0.0000036795004],"category_scores_gemma":[0.00048463524,0.00009162486,0.000069904694,0.0004548027,0.000194085,0.00025332472,0.00023527433,0.0002962487,0.000004932052],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082842525,0.0007950488,0.00024278248,0.00011853549,0.000037645335,0.000022963386,0.0011040426,0.00013500199,0.3611999,0.010164648,0.012462425,0.61363417],"study_design_scores_gemma":[0.0015901851,0.004747568,0.0014191247,0.00060248584,0.00007100032,0.00033100863,0.00022232484,0.599071,0.3258919,0.03224867,0.033166073,0.00063861074],"about_ca_topic_score_codex":8.612447e-7,"about_ca_topic_score_gemma":7.843296e-8,"teacher_disagreement_score":0.61299556,"about_ca_system_score_codex":0.000050196915,"about_ca_system_score_gemma":0.00008813981,"threshold_uncertainty_score":0.37363532},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008661858,0.000031611744,0.993889,0.0030611544,0.000121475976,0.00004609759,6.7555436e-7,0.0005498181,0.0014340017],"genre_scores_gemma":[0.18595304,0.000014347884,0.81285053,0.0009180659,0.00010398106,0.000015432472,0.000004823892,0.000010301972,0.000129477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978719,0.000025871597,0.00032897826,0.0005590096,0.0008307881,0.00038348848],"domain_scores_gemma":[0.9983027,0.00011798648,0.00010303452,0.0009366393,0.0001880155,0.00035163693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007620334,0.00016738578,0.00033220323,0.00036265235,0.00015713234,0.00021820354,0.0017663507,0.00014385323,0.00039603515],"category_scores_gemma":[0.00054198544,0.00014270154,0.00021835449,0.002070073,0.00032682094,0.0007682737,0.00054557767,0.0006722446,0.000104693245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013579314,0.0007092946,0.006775966,0.00008994345,0.0012949961,0.0013546861,0.0012078828,0.00030277204,0.06470112,0.010039628,0.010808019,0.9027021],"study_design_scores_gemma":[0.00011298964,0.0000144648375,0.00038331116,0.000009897393,0.00011787122,0.000017691227,0.0000108200675,0.9834045,0.0072071925,0.0061600967,0.002350816,0.0002103496],"about_ca_topic_score_codex":0.000054066673,"about_ca_topic_score_gemma":0.00006119747,"teacher_disagreement_score":0.9831017,"about_ca_system_score_codex":0.000020599755,"about_ca_system_score_gemma":0.000112935086,"threshold_uncertainty_score":0.58191997},"labels":[],"label_agreement":null},{"id":"W2006008489","doi":"10.1109/icip.2007.4379855","title":"PSF Recovery from Examples for Blind Super-Resolution","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Superresolution; Computer science; Computer vision; Frame (networking); Point spread function; Resolution (logic); Euclidean distance; Point (geometry); Function (biology); Image resolution; Series (stratigraphy); Optical transfer function; Image (mathematics); Pattern recognition (psychology); Mathematics","score_opus":0.04289826583657437,"score_gpt":0.313343239450811,"score_spread":0.27044497361423664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006008489","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020688628,0.00017473543,0.9945957,0.00043524304,0.00016405804,0.00017296705,0.000004170626,0.00077094545,0.0016133214],"genre_scores_gemma":[0.03314311,0.0000106403995,0.96574986,0.00041781962,0.000098308905,0.00001670145,0.0000083644745,0.000009559024,0.0005456545],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99905413,0.000013095936,0.00018719614,0.00034550647,0.00013364053,0.00026642552],"domain_scores_gemma":[0.99911654,0.00028295993,0.00005315085,0.00038680361,0.000107500906,0.000053060998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045778457,0.0000982608,0.000098551456,0.0000899898,0.000107461994,0.00012113925,0.0005414464,0.00006109084,0.000013656038],"category_scores_gemma":[0.00014165092,0.000090305286,0.000044069795,0.00018121353,0.000032686734,0.0010151855,0.00014565521,0.00006344048,0.000014285233],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000076195676,0.00010493009,0.00026911392,0.000016737285,0.000016030888,0.0000061324135,0.00027793343,0.000014902097,0.096205555,0.028979355,0.011085883,0.8629472],"study_design_scores_gemma":[0.00080138474,0.00022102568,0.0013834035,0.000044519413,0.000008510667,0.0000082471515,0.000041338273,0.06462898,0.30659407,0.56474584,0.061030686,0.0004919834],"about_ca_topic_score_codex":0.0000802487,"about_ca_topic_score_gemma":0.00006299884,"teacher_disagreement_score":0.86245525,"about_ca_system_score_codex":0.000050784198,"about_ca_system_score_gemma":0.000035656576,"threshold_uncertainty_score":0.36825424},"labels":[],"label_agreement":null},{"id":"W2009620901","doi":"10.1118/1.3021117","title":"Optimization of super‐resolution processing using incomplete image sets in PET imaging","year":2008,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Occupational and Environmental Medical Association of Canada","funders":"","keywords":"Medical imaging; Image processing; Image resolution; Iterative reconstruction; Computer vision; Image registration; Resolution (logic); Computer science; Pet imaging; Positron emission tomography; Artificial intelligence; Medical physics; Nuclear medicine; Image (mathematics); Medicine","score_opus":0.02602655143014419,"score_gpt":0.3016534115770466,"score_spread":0.2756268601469024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009620901","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068536056,0.00012351347,0.9922554,0.0002206982,0.000043424436,0.00008688411,9.1170153e-7,0.00020155343,0.00021399128],"genre_scores_gemma":[0.48708576,0.000012627642,0.5127731,0.00008239052,0.000029929648,0.0000032335804,0.0000030113754,0.000009125959,8.409617e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849176,0.00005311456,0.0003260723,0.0002966101,0.00057479064,0.00025767324],"domain_scores_gemma":[0.999319,0.00004261703,0.00014800343,0.00025077973,0.00016352156,0.00007607823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026407256,0.00012818229,0.00020554861,0.00009154347,0.00014295266,0.0000311464,0.0005331181,0.00003150977,0.000004750793],"category_scores_gemma":[0.00020926022,0.00013028434,0.000033392054,0.00067382253,0.00027154377,0.0014847495,0.00027718412,0.0002195789,0.0000012077634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006354918,0.0018649277,0.020119878,0.0013257947,0.000025085148,0.0015327891,0.008483522,0.066020854,0.07287642,0.002939623,0.00058970577,0.82415783],"study_design_scores_gemma":[0.00022655037,0.000010798536,0.00020180787,0.00021947593,0.000002093607,0.000090129826,0.000008493506,0.98571235,0.0068966327,0.006491522,0.0000074359777,0.00013271197],"about_ca_topic_score_codex":0.00004677738,"about_ca_topic_score_gemma":0.0000016448008,"teacher_disagreement_score":0.9196915,"about_ca_system_score_codex":0.00009203336,"about_ca_system_score_gemma":0.00023229756,"threshold_uncertainty_score":0.53128415},"labels":[],"label_agreement":null},{"id":"W2015919738","doi":"10.1016/j.jvcir.2014.01.007","title":"Bandlet-based sparsity regularization in video inpainting","year":2014,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Inpainting; Regularization (linguistics); Computer vision; Computer science; Artificial intelligence; Mathematics; Image (mathematics)","score_opus":0.017537634160742957,"score_gpt":0.33913948506798486,"score_spread":0.3216018509072419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015919738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017531538,0.00013857307,0.980044,0.001633451,0.000025843123,0.00007136554,7.419765e-8,0.000041927527,0.0005132053],"genre_scores_gemma":[0.5571614,0.00006846492,0.44260466,0.00013335403,0.0000135007995,0.0000015505352,0.0000020589296,0.000003882603,0.000011099212],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986955,0.00045468894,0.00042262662,0.00012394793,0.00021306222,0.00009017915],"domain_scores_gemma":[0.9984276,0.00027262495,0.0005623437,0.0003388077,0.00035733322,0.000041272913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014136718,0.00007361907,0.00014758228,0.00026272392,0.00010773914,0.00019867758,0.0004261866,0.000038346727,0.0000018031313],"category_scores_gemma":[0.00096842984,0.00007273589,0.00003183684,0.00038058904,0.00006956158,0.0018458933,0.00014672229,0.00020105779,7.7314337e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001537374,0.00075662666,0.027196629,0.00015679689,0.000024514695,0.000009612796,0.00311968,0.0007219249,0.28435746,0.022113066,0.0005926954,0.66079724],"study_design_scores_gemma":[0.0010853545,0.00019208645,0.020417023,0.00024868807,0.000008987588,0.000032828488,0.00011950953,0.88362926,0.052057296,0.041600253,0.00044316368,0.00016553348],"about_ca_topic_score_codex":0.000008853577,"about_ca_topic_score_gemma":0.0000024354893,"teacher_disagreement_score":0.88290733,"about_ca_system_score_codex":0.000045100747,"about_ca_system_score_gemma":0.000037942962,"threshold_uncertainty_score":0.29660836},"labels":[],"label_agreement":null},{"id":"W2017429172","doi":"10.1155/2010/425891","title":"MRI Superresolution Using Self‐Similarity and Image Priors","year":2010,"lang":"en","type":"article","venue":"International Journal of Biomedical Imaging","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":118,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Computer science; Artificial intelligence; Superresolution; Computer vision; Interpolation (computer graphics); Similarity (geometry); Prior probability; Segmentation; Pattern recognition (psychology); Image resolution; Process (computing); Image (mathematics); Bayesian probability","score_opus":0.0074030159261036675,"score_gpt":0.31244496997407023,"score_spread":0.3050419540479666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017429172","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03706553,0.00012916578,0.9541604,0.0071902545,0.0012517481,0.000031892996,0.0000015967105,0.0000942092,0.00007518722],"genre_scores_gemma":[0.3331605,0.000028535458,0.66626483,0.00027584488,0.00026150318,3.6237174e-7,5.2859656e-7,0.000005830105,0.0000020513164],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856263,0.000031534582,0.0003880488,0.00018055782,0.0006623344,0.00017489359],"domain_scores_gemma":[0.9987149,0.00007171349,0.0002747557,0.00013759431,0.00064423744,0.00015676073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064470706,0.00010511089,0.00013304164,0.0003366785,0.00007726863,0.000315165,0.0009800729,0.00004555278,0.000010787251],"category_scores_gemma":[0.0003662981,0.00009215317,0.000055792254,0.00015756444,0.0002515212,0.0020248818,0.00037625557,0.00050213665,0.0000013533686],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016198861,0.0002059552,0.00368243,0.000014812821,0.00005614685,0.0005034733,0.0006256926,0.0000012486063,0.8717561,0.0023283325,0.0008786079,0.119931],"study_design_scores_gemma":[0.0013813252,0.0000809225,0.004692537,0.0002862037,0.000035140605,0.0103024915,0.00007782455,0.8816679,0.03488012,0.044158198,0.021983976,0.00045334653],"about_ca_topic_score_codex":0.000009544452,"about_ca_topic_score_gemma":6.321855e-7,"teacher_disagreement_score":0.88166666,"about_ca_system_score_codex":0.00007442808,"about_ca_system_score_gemma":0.00015762163,"threshold_uncertainty_score":0.37578973},"labels":[],"label_agreement":null},{"id":"W2018193545","doi":"10.1109/icccas.2010.5581975","title":"Single-frame image interpolation using edge-adaptive RDWT","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Interpolation (computer graphics); Artificial intelligence; Computer science; Computer vision; Discrete wavelet transform; Enhanced Data Rates for GSM Evolution; Wavelet transform; Process (computing); Image scaling; Frame (networking); Image (mathematics); Image processing; Wavelet; Telecommunications","score_opus":0.02609185396982629,"score_gpt":0.2948783470551716,"score_spread":0.2687864930853453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018193545","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038287004,0.000013577336,0.9854133,0.000248245,0.00030935515,0.00010268366,4.520372e-7,0.0010284047,0.009055243],"genre_scores_gemma":[0.29213196,3.433477e-7,0.70748013,0.00019692548,0.000055418644,0.0000034630284,4.1060017e-7,0.000010676569,0.000120669545],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990356,0.000021358912,0.00018384079,0.00035816242,0.00016523205,0.0002358127],"domain_scores_gemma":[0.99905366,0.000052812564,0.00011150536,0.00051225716,0.00020422522,0.000065564345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015574366,0.00013831632,0.00011385417,0.00012469802,0.00011642557,0.0003013612,0.000701414,0.000073339026,0.000033157692],"category_scores_gemma":[0.00013747874,0.00012799187,0.000039111783,0.0003005073,0.000111490706,0.0023172374,0.00037614017,0.00029740363,0.00003118312],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024625763,0.000035156776,0.00003557461,0.0000031449008,0.0000022108231,0.000003446828,0.00016992321,7.474008e-7,0.9465278,0.016150689,0.00015245768,0.036916424],"study_design_scores_gemma":[0.00008685118,0.000057806807,0.00004945896,0.000022797718,0.0000029491684,0.00004049705,0.000019073652,0.67978036,0.24873586,0.07018448,0.0007956245,0.00022422704],"about_ca_topic_score_codex":0.000021255659,"about_ca_topic_score_gemma":0.00000989481,"teacher_disagreement_score":0.69779193,"about_ca_system_score_codex":0.00004126061,"about_ca_system_score_gemma":0.000050185077,"threshold_uncertainty_score":0.52193564},"labels":[],"label_agreement":null},{"id":"W2018459209","doi":"10.1117/1.3580750","title":"Single-image super-resolution based on Markov random field and contourlet transform","year":2011,"lang":"en","type":"article","venue":"Journal of Electronic Imaging","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Contourlet; Artificial intelligence; Markov random field; Pattern recognition (psychology); Wavelet transform; Computer vision; Top-hat transform; Computer science; Random field; Mathematics; Image resolution; Pixel; Image processing; Wavelet; Image (mathematics); Image segmentation; Image texture; Statistics","score_opus":0.00954816042315723,"score_gpt":0.23673797219136788,"score_spread":0.22718981176821065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018459209","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001094575,0.0009458488,0.9905329,0.0034143738,0.0000830692,0.00009482704,2.560616e-7,0.000083190745,0.0037509645],"genre_scores_gemma":[0.7121472,0.000050829713,0.28682092,0.0008971363,0.000047567046,0.000002479305,1.76051e-7,0.0000118751705,0.000021848757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987206,0.00006498957,0.00034952495,0.00019064067,0.0002547344,0.0004195239],"domain_scores_gemma":[0.99915916,0.00015212217,0.00022812732,0.00020625102,0.00017729559,0.00007704238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007073653,0.00015372706,0.00023030335,0.00023048383,0.00010849803,0.00012330292,0.00046320053,0.000034468074,0.000015119391],"category_scores_gemma":[0.00013002534,0.00013173364,0.00009080234,0.00016132343,0.00005805256,0.0013797999,0.00003272356,0.000446564,8.5551426e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015499255,0.00052703236,0.0004539022,0.00009085154,0.000060324634,0.00030466894,0.0017245278,0.000019600324,0.15425484,0.0049547465,0.0027653605,0.8332942],"study_design_scores_gemma":[0.007860438,0.0030264654,0.0002479654,0.00061773526,0.000081487706,0.0012904258,0.000077667566,0.5034613,0.40305665,0.072340615,0.0072055385,0.00073369464],"about_ca_topic_score_codex":0.000009253713,"about_ca_topic_score_gemma":0.0000032135747,"teacher_disagreement_score":0.83256054,"about_ca_system_score_codex":0.00015297595,"about_ca_system_score_gemma":0.00016999956,"threshold_uncertainty_score":0.53719425},"labels":[],"label_agreement":null},{"id":"W2019728470","doi":"10.1117/12.567435","title":"Experimental results of parallel multiframe blind deconvolution using wavelength diversity","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Government of Canada","keywords":"Deconvolution; Blind deconvolution; A priori and a posteriori; Computer science; Computer vision; Artificial intelligence; Path (computing); Algorithm; Point spread function; Wavelength; Iterative reconstruction; Pattern recognition (psychology); Optics; Physics","score_opus":0.02328569746248592,"score_gpt":0.26785008066159316,"score_spread":0.24456438319910723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019728470","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9712952,0.00012761001,0.026685478,0.00077758194,0.00015079678,0.00043660545,0.000024782421,0.00020659514,0.00029536005],"genre_scores_gemma":[0.46302456,0.000020574362,0.53683,0.000022176446,0.00005408608,0.000018485563,0.0000018619307,0.000016942882,0.000011323685],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997631,1.9385428e-8,0.0007392113,0.00050197303,0.0007488573,0.0003789501],"domain_scores_gemma":[0.99752945,0.00008260382,0.000648222,0.00010156846,0.0015433162,0.00009482363],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055898423,0.00029772564,0.00038713517,0.00013678794,0.00016558463,0.000096035146,0.0017803381,0.00018180042,0.0000010556128],"category_scores_gemma":[0.0004746786,0.00027309862,0.00044245634,0.0004073183,0.0003199947,0.0015774622,0.0008643771,0.00028372926,4.6630743e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016376757,0.00024767767,0.00007159965,0.00018791985,0.0001426065,1.5212636e-7,0.0008044311,0.00044467946,0.7315956,0.26578748,0.00013464234,0.00041949732],"study_design_scores_gemma":[0.0028832685,0.00034940155,0.00018224826,0.00039769578,0.000057735346,0.000021751412,0.0008407559,0.21088581,0.7743822,0.009497262,0.00012436592,0.00037753425],"about_ca_topic_score_codex":0.000030968567,"about_ca_topic_score_gemma":9.789733e-8,"teacher_disagreement_score":0.51014453,"about_ca_system_score_codex":0.00034368763,"about_ca_system_score_gemma":0.00006203236,"threshold_uncertainty_score":0.9999721},"labels":[],"label_agreement":null},{"id":"W2021347102","doi":"10.1145/2661229.2661260","title":"FlexISP","year":2014,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":297,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Pipeline (software); Artificial intelligence; Computer vision; Image (mathematics); Demosaicing; Noise reduction; Deconvolution; Representation (politics); Blind deconvolution; Image processing; Image restoration; Computer graphics (images); Algorithm; Color image","score_opus":0.018755852424484534,"score_gpt":0.2772893836884317,"score_spread":0.2585335312639472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021347102","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012064493,0.00002463283,0.99502784,0.002520808,0.0001593819,0.00006258455,0.0000010280921,0.0011175098,0.0009655937],"genre_scores_gemma":[0.30004805,0.000037905313,0.69829065,0.0014795244,0.000014495809,0.000023202065,4.083331e-7,0.000011022694,0.00009478246],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99912995,0.000039537565,0.00013310827,0.00030217168,0.00019932644,0.00019588612],"domain_scores_gemma":[0.9985249,0.00014226072,0.000042768777,0.0011479885,0.00007548639,0.00006661931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017510363,0.00011890685,0.00009768434,0.0002093146,0.00023456103,0.00008897305,0.0011602007,0.00006468587,0.0000073204055],"category_scores_gemma":[0.000056975492,0.00011586525,0.00007090682,0.00063768565,0.000075823154,0.0005243252,0.000013632145,0.0002582099,0.000033754877],"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":[0.000008859174,0.00022187375,0.00003785413,0.000025304344,0.000020449641,0.0000035568444,0.00026883848,0.00013920067,0.0027211814,0.18411706,0.00048590655,0.8119499],"study_design_scores_gemma":[0.0002538033,0.00024220519,0.00018350704,0.00004281916,0.000010703001,0.00002369349,0.000005551282,0.052482378,0.03054943,0.8839038,0.031941123,0.0003609839],"about_ca_topic_score_codex":0.0000036900494,"about_ca_topic_score_gemma":0.000004912368,"teacher_disagreement_score":0.81158894,"about_ca_system_score_codex":0.000015196967,"about_ca_system_score_gemma":0.000020893816,"threshold_uncertainty_score":0.47248477},"labels":[],"label_agreement":null},{"id":"W2021997596","doi":"10.1117/12.810219","title":"A kernel representation for exponential splines with global tension","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Spline interpolation; Interpolation (computer graphics); Box spline; Mathematics; Spline (mechanical); Algorithm; Kernel (algebra); Ringing artifacts; Exponential function; Trilinear interpolation; Applied mathematics; Stairstep interpolation; Ringing; Mathematical analysis; Computer science; Bilinear interpolation; Artificial intelligence; Computer vision; Discrete mathematics","score_opus":0.013577092592946574,"score_gpt":0.26683149563866904,"score_spread":0.25325440304572244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021997596","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83942556,0.00009943747,0.15280727,0.0057277586,0.00015048543,0.0008083591,0.00002026063,0.0004277045,0.0005331974],"genre_scores_gemma":[0.13416785,0.000031032985,0.8652308,0.00014138629,0.00020950151,0.00013355505,0.0000057722295,0.000024810997,0.000055301414],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977996,1.17934364e-8,0.0005656344,0.00056325394,0.0006828371,0.00038866076],"domain_scores_gemma":[0.99673325,0.00008789779,0.0004640633,0.000100123616,0.00252785,0.00008681038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039227624,0.00030436888,0.00036152877,0.000078894984,0.000116821044,0.00023038895,0.001422621,0.00013802604,8.2712245e-7],"category_scores_gemma":[0.00050217577,0.00023913565,0.00039164085,0.00049645506,0.00015270266,0.001497251,0.000181055,0.00017243648,4.0416688e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018094086,0.00013654925,0.00015350773,0.00018439097,0.00010840121,1.5062054e-7,0.000120602504,0.000055325807,0.51195997,0.4801991,0.0021721649,0.004728902],"study_design_scores_gemma":[0.0024969927,0.0014283187,0.0013415016,0.00071491615,0.00016817905,0.0000880887,0.00060012285,0.540868,0.38027415,0.06965073,0.0015971493,0.00077186327],"about_ca_topic_score_codex":0.0000038008632,"about_ca_topic_score_gemma":7.7566085e-8,"teacher_disagreement_score":0.7124235,"about_ca_system_score_codex":0.00014821257,"about_ca_system_score_gemma":0.000045187873,"threshold_uncertainty_score":0.97516686},"labels":[],"label_agreement":null},{"id":"W2027029680","doi":"10.1109/isspit.2007.4458189","title":"Enhanced Pixel-Based Video Frame Interpolation Algorithms","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Motion interpolation; Interpolation (computer graphics); Computer science; Computer vision; Artificial intelligence; Algorithm; Stairstep interpolation; Motion (physics); Bilinear interpolation; Multivariate interpolation; Block-matching algorithm; Video tracking; Video processing","score_opus":0.011932584433105636,"score_gpt":0.30034465656307163,"score_spread":0.288412072129966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027029680","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036096587,0.000039163282,0.99019265,0.00048111173,0.00016021928,0.00011018044,2.0629142e-7,0.0015705748,0.0070849014],"genre_scores_gemma":[0.3311901,7.9748213e-7,0.6674781,0.0011143293,0.000027444861,0.000006301917,9.671734e-7,0.0000076149095,0.00017437758],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988861,0.000016072037,0.00023741554,0.00035596316,0.00021915,0.00028531294],"domain_scores_gemma":[0.9990849,0.0001194998,0.00009662628,0.00047392846,0.00014905848,0.00007599927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039172676,0.00012732246,0.00010869141,0.0001577261,0.00008264082,0.00012844511,0.00067032163,0.00006791854,0.000026560128],"category_scores_gemma":[0.000110274355,0.000117084135,0.000040119052,0.00042658852,0.00005058424,0.000897663,0.0001372003,0.00014570793,0.000047737696],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013291975,0.0000832039,0.00008059165,0.000013688378,0.000004807536,0.000007041401,0.00028660317,0.0000118490925,0.4265024,0.01559028,0.00048180917,0.5569244],"study_design_scores_gemma":[0.00018167145,0.000082832,0.00023245062,0.000037566202,0.0000015139547,0.0000036696154,0.000011749749,0.40372187,0.5637503,0.02929233,0.0024537994,0.0002302414],"about_ca_topic_score_codex":0.0000068782674,"about_ca_topic_score_gemma":0.000004956538,"teacher_disagreement_score":0.55669415,"about_ca_system_score_codex":0.000060691294,"about_ca_system_score_gemma":0.000048979142,"threshold_uncertainty_score":0.47745526},"labels":[],"label_agreement":null},{"id":"W2029412897","doi":"10.1117/12.561091","title":"&lt;title&gt;Parallel multiframe blind deconvolution using wavelength diversity&lt;/title&gt;","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Deconvolution; Computer science; Blind deconvolution; A priori and a posteriori; Wavelength; Computer vision; Point spread function; Adaptive optics; Optics; Artificial intelligence; Path (computing); Ground truth; Algorithm; Physics","score_opus":0.018706496568679662,"score_gpt":0.2514221000461444,"score_spread":0.23271560347746473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029412897","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90419084,0.00031738848,0.08000182,0.0015529133,0.0004844055,0.0004963796,0.000018084877,0.00053175143,0.012406404],"genre_scores_gemma":[0.12970325,0.00007385629,0.8697356,0.00006081434,0.00015641248,0.000017414493,0.0000022033582,0.000028138995,0.00022230281],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986626,1.3012033e-8,0.00030757135,0.0003119059,0.00044728332,0.00027066036],"domain_scores_gemma":[0.9988345,0.000035937486,0.00021245156,0.00006704012,0.0007835981,0.00006648277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002554458,0.00019399113,0.00020712362,0.000097910546,0.00011507479,0.00010213009,0.001047583,0.0001396814,0.000012176321],"category_scores_gemma":[0.00021075748,0.00017782576,0.00025493116,0.00027125224,0.00014359414,0.0007179763,0.00041700838,0.0002132783,0.000011075422],"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":[0.000018021514,0.00007908572,0.000026328535,0.00014833064,0.000104343395,2.244402e-7,0.00016900814,0.00012350916,0.19571169,0.79919845,0.0020524866,0.0023685405],"study_design_scores_gemma":[0.0026470502,0.00034327878,0.0002567322,0.0008259451,0.00018865312,0.00007858902,0.00026380247,0.7715017,0.11517597,0.082789615,0.0247278,0.0012008417],"about_ca_topic_score_codex":0.0000028268373,"about_ca_topic_score_gemma":5.6534276e-8,"teacher_disagreement_score":0.78973377,"about_ca_system_score_codex":0.00022427196,"about_ca_system_score_gemma":0.00004652565,"threshold_uncertainty_score":0.72515243},"labels":[],"label_agreement":null},{"id":"W2030702779","doi":"10.1117/12.892379","title":"Predictive video decoding using GME and motion reliability","year":2011,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Decoding methods; Computer science; Reliability (semiconductor); Artificial intelligence; Computer vision; Motion (physics); Frame (networking); Motion estimation; Quarter-pixel motion; Reference frame; Motion field; Block-matching algorithm; Algorithm; Video processing; Video tracking; Telecommunications","score_opus":0.01931485405457263,"score_gpt":0.2471148544568433,"score_spread":0.22780000040227066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030702779","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9194941,0.00011005238,0.07792896,0.0005324037,0.00015109747,0.0004402019,0.000010523947,0.0003372843,0.0009954119],"genre_scores_gemma":[0.29932272,0.0000459576,0.7004184,0.000037018992,0.000076270524,0.000056147735,7.626822e-7,0.00002752379,0.000015178443],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788564,2.2925581e-8,0.00061641546,0.00058371254,0.0005393084,0.0003748956],"domain_scores_gemma":[0.99745953,0.00011414023,0.000454061,0.000100522906,0.0017664892,0.00010523643],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00085799367,0.00029644897,0.00035280574,0.00012035884,0.00013365468,0.00014724072,0.0013084396,0.00016724452,0.0000023553564],"category_scores_gemma":[0.00089748675,0.0002592559,0.00029499942,0.00038954016,0.00031834992,0.0021704675,0.00051608303,0.00030719902,3.3797372e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069862304,0.00013985526,0.0013111534,0.00049700064,0.00015127237,1.4816908e-7,0.0009821629,0.000035177985,0.50794566,0.4861264,0.0002536406,0.0024876846],"study_design_scores_gemma":[0.00063535623,0.0003351963,0.001402856,0.0004735108,0.000103013845,0.000042903386,0.0006490105,0.6382176,0.31211644,0.045341108,0.00022646462,0.0004565083],"about_ca_topic_score_codex":0.000011002079,"about_ca_topic_score_gemma":4.4666734e-8,"teacher_disagreement_score":0.63818246,"about_ca_system_score_codex":0.00019567182,"about_ca_system_score_gemma":0.000035041994,"threshold_uncertainty_score":0.999986},"labels":[],"label_agreement":null},{"id":"W2030877982","doi":"10.1109/iscas.2010.5537457","title":"Comparison of Haar wavelet-based and Poisson-based numerical integration techniques","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Haar wavelet; Wavelet; Poisson distribution; Computation; Haar; Computer science; Convergence (economics); Decomposition; Algorithm; Image (mathematics); Wavelet transform; Wavelet packet decomposition; Mathematics; Mathematical optimization; Applied mathematics; Computer vision; Artificial intelligence; Discrete wavelet transform; Statistics","score_opus":0.01628314003304467,"score_gpt":0.3361222118472482,"score_spread":0.31983907181420357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030877982","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021459542,0.000023103381,0.9947543,0.0011891894,0.000042226857,0.00014404251,8.3158466e-7,0.0009185568,0.0007817985],"genre_scores_gemma":[0.48603708,2.827382e-7,0.51374525,0.00018218045,0.000007112093,0.000012158296,0.0000014884063,0.000005286783,0.000009175585],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990308,0.000030464715,0.0002735896,0.00030304398,0.00020503369,0.00015703784],"domain_scores_gemma":[0.9990274,0.0001256033,0.00015548845,0.00044082024,0.00018698322,0.00006373921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021981381,0.000137602,0.00021981467,0.00014139492,0.00006750354,0.000083069084,0.00047282918,0.000090411355,0.000011316648],"category_scores_gemma":[0.00015733286,0.000113890106,0.000033902714,0.00028187176,0.00013981627,0.00037489395,0.00008234259,0.00026450248,0.0000015871149],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011935893,0.00019651966,0.0018229915,0.000031871212,0.000002134686,0.0000014132432,0.00008951453,0.0000014144794,0.4716167,0.024881402,0.0005554599,0.5007886],"study_design_scores_gemma":[0.00006532761,0.00009701773,0.00026857053,0.000019478917,0.0000016656699,0.0000011882669,0.0000039113665,0.37728852,0.61871433,0.0030301532,0.0004162368,0.0000935657],"about_ca_topic_score_codex":0.000031152704,"about_ca_topic_score_gemma":0.000009822439,"teacher_disagreement_score":0.5006951,"about_ca_system_score_codex":0.000015699583,"about_ca_system_score_gemma":0.00008322808,"threshold_uncertainty_score":0.4644304},"labels":[],"label_agreement":null},{"id":"W2037804396","doi":"10.1016/j.patcog.2011.08.023","title":"Reducing aliasing in images: a PDE-based diffusion revisited","year":2011,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Aliasing; Hessian matrix; Anisotropic diffusion; Grayscale; Diffusion; Eigenvalues and eigenvectors; Mathematics; Algorithm; Computer science; Matrix (chemical analysis); Artificial intelligence; Computer vision; Image (mathematics); Filter (signal processing); Applied mathematics; Physics","score_opus":0.05248244583550957,"score_gpt":0.2746895210236158,"score_spread":0.22220707518810626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037804396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04543695,0.00006386978,0.9525724,0.000114962684,0.00007700244,0.00018315518,0.0000033887113,0.00051818846,0.0010300681],"genre_scores_gemma":[0.5779731,0.00001325125,0.42150608,0.00041544854,0.000023241673,0.00003342509,0.000015553294,0.000013667695,0.00000620033],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987394,0.0001039876,0.00028938203,0.00044297054,0.00016569319,0.0002585279],"domain_scores_gemma":[0.999287,0.00004569701,0.00016103993,0.00034510018,0.00011087382,0.00005030539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031200566,0.00014830356,0.00014737903,0.00028915,0.000074556054,0.000094503725,0.0003588118,0.000060563794,0.000042869393],"category_scores_gemma":[0.00011003752,0.00015356147,0.000041248335,0.0004263363,0.000030447365,0.0009260973,0.00011572784,0.00017638023,0.00005698426],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010096258,0.000115611605,0.003329418,0.00006941524,0.0000017316892,0.000044696975,0.0007700512,5.688227e-7,0.026160782,0.0000037761808,0.00006237951,0.96943146],"study_design_scores_gemma":[0.0017364708,0.00028387795,0.029371653,0.0048072212,0.000023692375,0.00007447612,0.00009256817,0.20035422,0.7118645,0.050034877,0.0001077132,0.0012487591],"about_ca_topic_score_codex":0.00012474197,"about_ca_topic_score_gemma":0.0000049794835,"teacher_disagreement_score":0.96818274,"about_ca_system_score_codex":0.0000791572,"about_ca_system_score_gemma":0.000026352645,"threshold_uncertainty_score":0.6262055},"labels":[],"label_agreement":null},{"id":"W2050885079","doi":"10.1109/icip.2007.4379853","title":"Fast Generalized Motion Estimation and Superresolution","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Motion estimation; Robustness (evolution); Superresolution; Interpolation (computer graphics); Computer science; Algorithm; Scaling; Convergence (economics); Pixel; Image resolution; Artificial intelligence; Computer vision; Motion (physics); Mathematics; Image (mathematics)","score_opus":0.012796746692807464,"score_gpt":0.2842029512766123,"score_spread":0.2714062045838049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050885079","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054495307,0.00007159536,0.99247426,0.0002801665,0.000043964195,0.00006281892,8.424464e-8,0.0006195203,0.0009980802],"genre_scores_gemma":[0.31819463,0.000005015755,0.68156856,0.00010147765,0.000010643039,0.0000019745446,0.0000010969322,0.0000024423139,0.00011416118],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948466,0.000010508286,0.0001068743,0.00017234158,0.00010031884,0.00012531501],"domain_scores_gemma":[0.9997168,0.000016852762,0.000030493668,0.00015219752,0.00004802271,0.00003559908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002654757,0.000056872155,0.000048127513,0.0000753094,0.000079224716,0.00007810692,0.00013158495,0.000029457942,0.0000026301004],"category_scores_gemma":[0.000036473637,0.000052112053,0.000008963454,0.00015430734,0.000028974644,0.00094934145,0.0000779694,0.000039686325,0.000004856473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002493462,0.000013715871,0.00018304957,0.0000062516824,9.742919e-7,0.0000017669362,0.00013461626,0.00003756478,0.027228335,0.06582544,0.00013103042,0.9064348],"study_design_scores_gemma":[0.00012520366,0.000020120842,0.0022901732,0.0000070705423,0.0000011660729,0.0000211973,0.000005696292,0.92549014,0.042620346,0.029077083,0.00025208763,0.00008971139],"about_ca_topic_score_codex":0.000011864118,"about_ca_topic_score_gemma":0.0000043770083,"teacher_disagreement_score":0.9254526,"about_ca_system_score_codex":0.000028369435,"about_ca_system_score_gemma":0.0000069833445,"threshold_uncertainty_score":0.21250679},"labels":[],"label_agreement":null},{"id":"W2051422030","doi":"10.1109/tip.2006.888333","title":"Matching Pursuit-Based Region-of-Interest Image Coding","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computational complexity theory; Computer science; Matching pursuit; Encoder; Coding (social sciences); Artificial intelligence; Computer vision; Image quality; Image (mathematics); Region of interest; Multiresolution analysis; Pattern recognition (psychology); Algorithm; Mathematics; Wavelet transform; Wavelet; Compressed sensing","score_opus":0.0410807255848694,"score_gpt":0.313923288373438,"score_spread":0.2728425627885686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051422030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014386296,0.000102681384,0.9953132,0.00048638287,0.00026596422,0.00021926571,0.000003157537,0.001277544,0.0008931181],"genre_scores_gemma":[0.5052976,0.00000525465,0.49439105,0.00018534402,0.000022807568,0.000014199848,4.7223722e-7,0.00003256519,0.0000507165],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975888,0.000052834217,0.00065301696,0.0006849198,0.00040799243,0.00061240356],"domain_scores_gemma":[0.9981037,0.00023593297,0.0004038871,0.00067408924,0.0004231509,0.00015923475],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007424308,0.00035342047,0.0003403129,0.00062434806,0.0005385091,0.00051438465,0.0012329033,0.000120107354,0.00000962549],"category_scores_gemma":[0.000029030567,0.00036531748,0.00016081061,0.0010459117,0.00029337648,0.0028084137,0.000012124974,0.0005868484,0.00001594363],"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":[0.00007933676,0.00032988528,0.000003854332,0.0004376617,0.00001479665,0.00016213303,0.00089914154,0.00021122741,0.5329718,0.00047947894,0.000051554354,0.46435913],"study_design_scores_gemma":[0.00047443743,0.00011081656,0.000013666005,0.0008683614,0.000023724893,0.00011983868,0.00014887472,0.05284111,0.9369058,0.007951167,0.00007642945,0.00046576586],"about_ca_topic_score_codex":0.000018758397,"about_ca_topic_score_gemma":0.000014614142,"teacher_disagreement_score":0.5038589,"about_ca_system_score_codex":0.0001664242,"about_ca_system_score_gemma":0.00020794805,"threshold_uncertainty_score":0.9998799},"labels":[],"label_agreement":null},{"id":"W2057065563","doi":"10.1109/tip.2014.2305844","title":"A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":414,"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":"Intel Collaboration Research Institute for Computational Intelligence; Azrieli Foundation","keywords":"Computer science; Artificial intelligence; Computational complexity theory; Cluster analysis; Image (mathematics); Pattern recognition (psychology); Iterative reconstruction; Resolution (logic); Artificial neural network; Algorithm; Image resolution","score_opus":0.027032290316827055,"score_gpt":0.3031759886557877,"score_spread":0.27614369833896063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057065563","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000067610395,0.000011741121,0.9955841,0.00089680107,0.00018704114,0.0005355312,0.00010829335,0.0014189327,0.0011899603],"genre_scores_gemma":[0.38981473,0.0000016708581,0.60941726,0.0003012847,0.000038825823,0.00029127585,0.000012667132,0.00003859182,0.0000836982],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99752736,0.000102654376,0.00047429506,0.00090401695,0.00048746794,0.0005041786],"domain_scores_gemma":[0.9981846,0.0003439689,0.00016954755,0.0006772017,0.0004658085,0.00015882961],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004198516,0.00032204867,0.00025629526,0.00039533884,0.0008691662,0.0005867786,0.0005031901,0.00011941048,0.0000089776],"category_scores_gemma":[0.00016752501,0.00033879193,0.00011463681,0.00052168296,0.00021845712,0.0022957062,0.000005698156,0.00035480573,0.000017119084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030225888,0.0017673615,0.0000035183525,0.00041367943,0.000018262806,0.000007787841,0.00061083224,0.20371197,0.4121841,0.0014755063,0.0010751119,0.37842962],"study_design_scores_gemma":[0.0006576608,0.00033469353,0.000006339228,0.0001541161,0.00003419924,0.0000113432525,0.000015499161,0.86532265,0.12297439,0.010127393,0.000086068016,0.00027567626],"about_ca_topic_score_codex":0.000006551798,"about_ca_topic_score_gemma":0.000003670941,"teacher_disagreement_score":0.66161066,"about_ca_system_score_codex":0.0002212124,"about_ca_system_score_gemma":0.00019759007,"threshold_uncertainty_score":0.9999064},"labels":[],"label_agreement":null},{"id":"W2057542768","doi":"10.1137/090771260","title":"Image Sharpening via Sobolev Gradient Flows","year":2010,"lang":"en","type":"article","venue":"SIAM Journal on Imaging Sciences","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Sharpening; Sobolev space; Mathematics; Metric (unit); Mathematical analysis; Uniqueness; Balanced flow; Smoothing; Isotropy; Applied mathematics; Computer science; Artificial intelligence; Physics","score_opus":0.010997945721144113,"score_gpt":0.29525086823011903,"score_spread":0.28425292250897494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057542768","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009948133,0.0001769837,0.97589105,0.005912963,0.0025987732,0.00009138598,6.1715645e-7,0.0005059918,0.004874112],"genre_scores_gemma":[0.26149154,0.000013777533,0.73729414,0.0008796542,0.00024252779,0.0000052881,1.5387795e-7,0.000012833675,0.000060071245],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972764,0.00007023712,0.00040879365,0.0006281226,0.00089171587,0.0007247062],"domain_scores_gemma":[0.9985314,0.00012630742,0.00035183472,0.00048222058,0.00023934848,0.0002688924],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0019755836,0.0002570446,0.00021258264,0.000495847,0.0014331776,0.002227372,0.0030606661,0.000035526326,0.000036003727],"category_scores_gemma":[0.00032896167,0.0002018545,0.00011547144,0.0008951215,0.00055541296,0.004311479,0.00034848382,0.0010552774,0.00007688407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000780302,0.0001430175,0.0016652822,0.000015198762,0.00000894502,0.0003376289,0.0008489654,0.00008162968,0.61627394,0.012800795,0.004034589,0.3637822],"study_design_scores_gemma":[0.00042683425,0.00026504564,0.0009492538,0.00023611302,0.000009073093,0.0040373527,0.00008645921,0.7361543,0.040924903,0.20431699,0.011751675,0.0008419763],"about_ca_topic_score_codex":0.000005311647,"about_ca_topic_score_gemma":0.0000021866526,"teacher_disagreement_score":0.7360727,"about_ca_system_score_codex":0.000057143363,"about_ca_system_score_gemma":0.00020939011,"threshold_uncertainty_score":0.99986684},"labels":[],"label_agreement":null},{"id":"W2057792073","doi":"10.1016/j.jvcir.2012.07.001","title":"Model-based adaptive resolution upconversion of degraded images","year":2012,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Upsampling; Pixel; Computer science; Deconvolution; Autoregressive model; Piecewise; Algorithm; Artificial intelligence; Mathematics; Image (mathematics); Mathematical optimization; Computer vision","score_opus":0.054583968003163924,"score_gpt":0.3791078921176934,"score_spread":0.32452392411452946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057792073","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011986312,0.0012536127,0.98575795,0.0005706301,0.00003311323,0.00008659727,7.5363295e-7,0.000035050874,0.00027600944],"genre_scores_gemma":[0.53072894,0.00020368276,0.4690087,0.00003232211,0.0000104946475,0.0000023154316,0.0000015667214,0.0000038584367,0.000008080804],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882877,0.00026224327,0.00042476808,0.000092410875,0.0002743717,0.00011745536],"domain_scores_gemma":[0.9979823,0.00017568046,0.00081079523,0.00034474584,0.0006135546,0.00007290996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074298936,0.00008755128,0.00016834331,0.00022077652,0.00010819571,0.00005679645,0.00041168169,0.00004288267,0.000002466743],"category_scores_gemma":[0.0002426199,0.00008053501,0.000060763145,0.00026263786,0.00014340857,0.0032636146,0.00016488624,0.00017843502,7.623782e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042185618,0.0011915383,0.0042442684,0.00012685325,0.000061134386,0.000002401799,0.0041542267,0.0008177702,0.8195411,0.008162424,0.0026246079,0.1586518],"study_design_scores_gemma":[0.00058298087,0.00021095984,0.0032016898,0.000118572505,0.000023482178,0.000033576616,0.0002845813,0.6998093,0.29090086,0.0046791956,0.000043664837,0.00011113157],"about_ca_topic_score_codex":0.000007917335,"about_ca_topic_score_gemma":2.0983975e-7,"teacher_disagreement_score":0.69899154,"about_ca_system_score_codex":0.000052397885,"about_ca_system_score_gemma":0.000061415085,"threshold_uncertainty_score":0.32841223},"labels":[],"label_agreement":null},{"id":"W2061539841","doi":"10.1109/tip.2013.2290586","title":"A MAP-Based Image Interpolation Method via Viterbi Decoding of Markov Chains of Interpolation Functions","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Interpolation (computer graphics); Stairstep interpolation; Demosaicing; Image scaling; Nearest-neighbor interpolation; Bilinear interpolation; Mathematics; Algorithm; Bicubic interpolation; Artificial intelligence; Viterbi algorithm; Computer science; Linear interpolation; Pattern recognition (psychology); Computer vision; Image processing; Decoding methods; Image (mathematics); Color image","score_opus":0.012179552561419353,"score_gpt":0.2962502302134458,"score_spread":0.2840706776520265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061539841","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025019996,0.00004254608,0.99792427,0.0003081987,0.00026051287,0.00025956545,0.000013148999,0.00053379196,0.00040777226],"genre_scores_gemma":[0.4498272,0.0000014608449,0.549976,0.0000720649,0.000017938748,0.000035367386,0.000002579949,0.000025684165,0.000041708918],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977062,0.00020964096,0.0007800906,0.0005875758,0.00038834487,0.00032819615],"domain_scores_gemma":[0.9977258,0.0002985106,0.00066980557,0.00060697465,0.00060803344,0.00009089296],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007725082,0.00031088546,0.0004039742,0.0008196784,0.00029864372,0.00017857658,0.00061084196,0.0001204544,0.000033011944],"category_scores_gemma":[0.000060725975,0.00032535158,0.00018530399,0.00085655774,0.00019012208,0.0024620572,0.000012527554,0.00036952662,0.000008826984],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005943909,0.00018589363,0.000011055321,0.00036246824,0.0000140817665,7.5468546e-7,0.0005049109,0.00030464132,0.6388972,0.00004503158,0.000015847387,0.35959873],"study_design_scores_gemma":[0.00029588537,0.00013251478,0.000015944179,0.00037396493,0.000029763967,0.000008362896,0.00003695928,0.62707496,0.37056682,0.0012588724,0.000021824138,0.00018415625],"about_ca_topic_score_codex":0.000022586875,"about_ca_topic_score_gemma":0.000009639091,"teacher_disagreement_score":0.6267703,"about_ca_system_score_codex":0.00009815512,"about_ca_system_score_gemma":0.00013052438,"threshold_uncertainty_score":0.99991983},"labels":[],"label_agreement":null},{"id":"W2066384553","doi":"10.1109/icip.2011.6116437","title":"Reducing aliasing in images: A simple diffusion equation based on the inverse diffusivity","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Aliasing; Anti-aliasing; Diffusion; Algorithm; Curvature; Computer science; Simple (philosophy); Thermal diffusivity; Filter (signal processing); Anisotropic diffusion; Diffusion equation; Inverse; SIMPLE algorithm; Mathematics; Artificial intelligence; Computer vision; Image (mathematics); Geometry; Physics; Speech recognition","score_opus":0.06080980718834418,"score_gpt":0.2774293133345244,"score_spread":0.21661950614618025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066384553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02827581,0.0000040941977,0.96485,0.00066711946,0.00004371624,0.00018838047,3.1478092e-7,0.00040480896,0.005565734],"genre_scores_gemma":[0.6042364,0.0000014824868,0.39479026,0.00090068456,0.000008231173,0.000018828192,6.4471874e-7,0.0000069186394,0.000036545025],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988709,0.00012689587,0.00019487509,0.00035508536,0.00022012844,0.00023213313],"domain_scores_gemma":[0.99897176,0.00019624093,0.00010535362,0.0006319512,0.00005560613,0.00003908459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005560991,0.00012955077,0.00010351408,0.0001494273,0.00015202597,0.00009738442,0.0006143116,0.000040619954,0.000036707654],"category_scores_gemma":[0.00039676114,0.0000892783,0.000033110064,0.0004714351,0.000058887905,0.0007423396,0.00022759542,0.00017024338,0.0000129850905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016698755,0.0023711172,0.014745299,0.00013976922,0.000012139254,0.00013192868,0.019019032,0.0009988587,0.43575835,0.050856438,0.007127474,0.46867263],"study_design_scores_gemma":[0.00017198542,0.00005038791,0.0023973214,0.000071577626,0.0000013849736,0.0000012459333,0.000051551346,0.892812,0.06935966,0.03489423,0.000047708527,0.00014092849],"about_ca_topic_score_codex":0.00034234,"about_ca_topic_score_gemma":0.000046627505,"teacher_disagreement_score":0.89181316,"about_ca_system_score_codex":0.00009333821,"about_ca_system_score_gemma":0.00005004307,"threshold_uncertainty_score":0.36406636},"labels":[],"label_agreement":null},{"id":"W2069485629","doi":"10.1109/icip.2012.6467542","title":"An efficient projected subgradient algorithm for blind image deconvolution using an L&lt;inf&gt;1&lt;/inf&gt;-TV cost function","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Subgradient method; Algorithm; Computer science; Norm (philosophy); Mathematics; Artificial intelligence; Mathematical optimization","score_opus":0.03560355828277109,"score_gpt":0.32135098139423746,"score_spread":0.2857474231114664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069485629","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027124714,0.00023742198,0.9681575,0.000050568204,0.0007460989,0.001621867,0.000015492333,0.0018287773,0.00021758392],"genre_scores_gemma":[0.18226089,0.0000055933224,0.8168694,0.00015769113,0.0003256917,0.0002002371,0.000057987698,0.000050719675,0.00007184181],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996799,0.00014127977,0.0005627395,0.0009335828,0.0004945424,0.00106889],"domain_scores_gemma":[0.9973696,0.00007759024,0.00035157896,0.0010471691,0.00076253084,0.00039155778],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010676342,0.00042496313,0.0003273739,0.00044052274,0.00061525556,0.00051743235,0.00084106205,0.00018582425,0.000020969272],"category_scores_gemma":[0.000102066435,0.00040135797,0.00011101891,0.00091958797,0.00015886275,0.004680569,0.00023431564,0.00021049548,0.000019481395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014937065,0.0024824652,0.00025728988,0.000090925205,0.00005137009,0.000005938117,0.0017747987,0.000776792,0.38838154,0.008761014,0.0008838005,0.5963847],"study_design_scores_gemma":[0.00086267985,0.00050931633,0.00059159595,0.000035566733,0.000041460913,0.000045842793,0.000046233337,0.950902,0.042832416,0.0010993428,0.0024705394,0.00056304614],"about_ca_topic_score_codex":0.00003954591,"about_ca_topic_score_gemma":0.000024303425,"teacher_disagreement_score":0.95012516,"about_ca_system_score_codex":0.000494361,"about_ca_system_score_gemma":0.00019979312,"threshold_uncertainty_score":0.99984384},"labels":[],"label_agreement":null},{"id":"W2071498019","doi":"10.1109/ism.2011.25","title":"Hybrid Video Compression Using Selective Keyframe Identification and Patch-Based Super-Resolution","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Video compression picture types; Data compression; Computer vision; Residual frame; Artificial intelligence; Multiview Video Coding; Pipeline (software); Key (lock); Frame (networking); Compression (physics); Video tracking; Video processing; Reference frame; Telecommunications","score_opus":0.03929182724049538,"score_gpt":0.2804806056701463,"score_spread":0.24118877842965095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071498019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03251167,0.00009263001,0.9661273,0.00008621592,0.00006499187,0.0001632633,0.0000010215875,0.0006497137,0.0003032413],"genre_scores_gemma":[0.52378047,0.0000022314427,0.47608575,0.00009059648,0.000007266082,0.000008425425,0.0000013272686,0.0000060173693,0.00001792575],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989347,0.00006529021,0.00020449075,0.00042586014,0.00017206282,0.00019762706],"domain_scores_gemma":[0.99921834,0.000037233833,0.000114775314,0.00036832708,0.00020101626,0.000060314847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001997983,0.00012584927,0.00011139263,0.00012330482,0.00020611136,0.00012554631,0.000349108,0.000042438074,0.000008664499],"category_scores_gemma":[0.000059325488,0.00011667851,0.00002284525,0.00020852436,0.00008841141,0.0013466205,0.00015942214,0.000112671216,0.0000067700867],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059498252,0.00032650208,0.004153713,0.000086570275,0.00001602247,0.00001787999,0.001569973,0.000109320776,0.9156613,0.0102533735,0.0011866603,0.066559196],"study_design_scores_gemma":[0.00010353382,0.000031867963,0.002021833,0.000034390465,0.000003978444,0.000011522449,0.0000064895676,0.58457035,0.39153907,0.021501368,0.000051861647,0.00012372828],"about_ca_topic_score_codex":0.00017561382,"about_ca_topic_score_gemma":0.0000037026068,"teacher_disagreement_score":0.58446103,"about_ca_system_score_codex":0.00006911388,"about_ca_system_score_gemma":0.000053326323,"threshold_uncertainty_score":0.47580114},"labels":[],"label_agreement":null},{"id":"W2078549882","doi":"10.1109/vcip.2014.7051560","title":"GPU-aided real-time image/video super resolution based on error feedback","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Interpolation (computer graphics); Artificial intelligence; Computer vision; Convolution (computer science); Compensation (psychology); Pixel; Sub-pixel resolution; Enhanced Data Rates for GSM Evolution; Process (computing); Image resolution; Resolution (logic); Image (mathematics); Image processing; Digital image processing; Artificial neural network","score_opus":0.01278934811919855,"score_gpt":0.26894129529783306,"score_spread":0.2561519471786345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078549882","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004593333,0.000006251889,0.93374467,0.0031457273,0.000079214544,0.00016725338,9.3733047e-7,0.0021751453,0.060221467],"genre_scores_gemma":[0.042548824,0.0000031520449,0.9522935,0.0017022856,0.00006576775,0.000029413051,0.0000052403475,0.000027364342,0.0033244304],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981057,0.0001403298,0.00028023135,0.0006571445,0.00039319275,0.0004234256],"domain_scores_gemma":[0.9982006,0.0002025484,0.00010042462,0.0011722004,0.00020259811,0.00012162155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005840524,0.0002294029,0.00021692891,0.00017874331,0.00017951611,0.00023195293,0.0009944609,0.00009012774,0.00011620255],"category_scores_gemma":[0.00036614636,0.00020012804,0.00006252188,0.00041909504,0.000109241206,0.0011890819,0.00022014993,0.00017149482,0.0006637003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012217838,0.00041461614,0.00014662273,0.000098093275,0.000013265661,0.000024014687,0.0002650673,0.000455488,0.7362492,0.043824986,0.132511,0.08587544],"study_design_scores_gemma":[0.00038051864,0.00025095997,0.00043077153,0.00006698027,0.0000041869107,0.0000054251605,0.0000026382315,0.9128774,0.0609493,0.019805541,0.00488961,0.00033670332],"about_ca_topic_score_codex":0.000043972952,"about_ca_topic_score_gemma":0.000003273774,"teacher_disagreement_score":0.9124219,"about_ca_system_score_codex":0.000103598206,"about_ca_system_score_gemma":0.00007250085,"threshold_uncertainty_score":0.8530746},"labels":[],"label_agreement":null},{"id":"W2079624778","doi":"10.1049/iet-ipr.2014.0313","title":"Single‐image super‐resolution in RGB space via group sparse representation","year":2014,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sparse approximation; Artificial intelligence; RGB color model; Representation (politics); Computer science; Group (periodic table); Computer vision; Image (mathematics); Space (punctuation); Pattern recognition (psychology); Resolution (logic); Mathematics; Physics","score_opus":0.019278105481546463,"score_gpt":0.28629419622135294,"score_spread":0.2670160907398065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079624778","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004121826,0.00036604068,0.9895203,0.0011744035,0.00013826857,0.00026940255,9.115761e-7,0.0012155472,0.0031933144],"genre_scores_gemma":[0.3897466,0.000010325665,0.60980386,0.00020774492,0.00008932757,0.0000399775,0.0000070218016,0.00003397487,0.00006116454],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99702483,0.00020608428,0.0005487874,0.0010089274,0.0005223597,0.0006890134],"domain_scores_gemma":[0.99835086,0.00011297101,0.0003261251,0.00078133936,0.00030023727,0.00012849494],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000826338,0.00034754787,0.00034756222,0.00038598193,0.00028514746,0.0009345491,0.0010085735,0.0001313215,0.000007702164],"category_scores_gemma":[0.0005808499,0.00036950814,0.000072396106,0.0013002651,0.00023312934,0.0067568277,0.00043019836,0.00040066967,0.000045053977],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002319567,0.00022305945,0.00056032825,0.00017562327,0.0000031592888,0.0000385976,0.0010261722,0.00003961407,0.7121733,0.0006395376,0.00030797033,0.28478947],"study_design_scores_gemma":[0.00079392194,0.00015218982,0.0012703517,0.0004419351,0.000012478685,0.00010504318,0.00009628535,0.76539934,0.17944248,0.05041237,0.0011255157,0.0007480594],"about_ca_topic_score_codex":0.00006062652,"about_ca_topic_score_gemma":0.000024878373,"teacher_disagreement_score":0.76535976,"about_ca_system_score_codex":0.00022054295,"about_ca_system_score_gemma":0.0000713583,"threshold_uncertainty_score":0.99987566},"labels":[],"label_agreement":null},{"id":"W2081057241","doi":"10.1109/icdsp.2009.5201145","title":"Storage-efficient quasi-Newton algorithms for image super-resolution","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Algorithm; Computer science; Broyden–Fletcher–Goldfarb–Shanno algorithm; Grayscale; Image resolution; Minification; Image (mathematics); Resolution (logic); Superresolution; Artificial intelligence; Computer vision","score_opus":0.020759051521966155,"score_gpt":0.3105352770627455,"score_spread":0.2897762255407793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081057241","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021634321,0.00014665611,0.9922605,0.0041573877,0.00013741947,0.00035307292,0.0000024232593,0.0014797845,0.001246392],"genre_scores_gemma":[0.03225601,0.0000053285426,0.96605825,0.0006730559,0.00006800835,0.0000357906,0.0000038425133,0.000010551315,0.00088913675],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986194,0.0000245512,0.00022344958,0.0004908445,0.00024722837,0.00039457253],"domain_scores_gemma":[0.9990293,0.000052223742,0.000071325056,0.00054243073,0.0002186553,0.00008611101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030812595,0.00016756242,0.00015573713,0.00012382257,0.00019774771,0.00020941501,0.0007634933,0.000060427457,0.0000069882904],"category_scores_gemma":[0.000099778816,0.00015099221,0.000074815616,0.00032364135,0.000048222835,0.0007566608,0.000108097905,0.000101945836,0.000020318339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029699557,0.0010134407,0.0000050413564,0.00004531089,0.000009525999,0.000028283122,0.0007299216,0.00057452,0.13027005,0.15220776,0.03171557,0.6833709],"study_design_scores_gemma":[0.00024869764,0.00033802132,0.00006291118,0.000017206272,0.0000031865343,0.000017198401,0.000012286494,0.9348371,0.032333747,0.025528658,0.0063641067,0.00023689034],"about_ca_topic_score_codex":0.0000073726637,"about_ca_topic_score_gemma":7.637867e-7,"teacher_disagreement_score":0.9342626,"about_ca_system_score_codex":0.00011072576,"about_ca_system_score_gemma":0.000058305206,"threshold_uncertainty_score":0.6157284},"labels":[],"label_agreement":null},{"id":"W2082418483","doi":"10.1109/acvmot.2005.58","title":"High-Resolution Video Synthesis from Mixed-Resolution Video Based on the Estimate-and-Correct Method","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Motion blur; Sub-pixel resolution; Frame rate; Resolution (logic); Pixel; Image resolution; Frame (networking); Motion estimation; Reduction (mathematics); Consistency (knowledge bases); Image processing; Image (mathematics); Mathematics; Telecommunications; Digital image processing","score_opus":0.016749741310290597,"score_gpt":0.2843330416916054,"score_spread":0.2675833003813148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082418483","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00056357967,0.00020785061,0.98484004,0.011753422,0.00016740125,0.00023711774,0.0000073896676,0.0013726631,0.0008505379],"genre_scores_gemma":[0.22875944,0.000013100914,0.7694819,0.0014729727,0.00007868448,0.00010452101,0.000002804187,0.000019447409,0.000067150795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775124,0.0003535324,0.00033319584,0.000739478,0.00042583895,0.0003967108],"domain_scores_gemma":[0.99635017,0.0022896163,0.00017987168,0.0009778559,0.00010676321,0.00009572695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010177438,0.00028738077,0.00026346787,0.0001602428,0.00039517088,0.00030404673,0.00096216897,0.00011634958,0.00006875654],"category_scores_gemma":[0.0010529367,0.00020164113,0.000079092926,0.00039632298,0.000106161395,0.0009475514,0.00028178573,0.00025493657,0.00008585544],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006526161,0.00016002134,0.000065154476,0.000015391119,0.000023058083,0.0000061154133,0.00013211757,0.012559591,0.019692656,0.0261315,0.011896994,0.92925215],"study_design_scores_gemma":[0.00011918959,0.00004572979,0.0003344256,0.00008267785,0.000017575709,0.0000042015545,0.0000055357777,0.81689775,0.16505043,0.015415701,0.0018011566,0.00022562205],"about_ca_topic_score_codex":0.00044939172,"about_ca_topic_score_gemma":0.000056378954,"teacher_disagreement_score":0.92902654,"about_ca_system_score_codex":0.00016356379,"about_ca_system_score_gemma":0.00006825834,"threshold_uncertainty_score":0.82226866},"labels":[],"label_agreement":null},{"id":"W2089058832","doi":"10.1109/icip.2011.6115637","title":"A structure-guided conditional sampling model for video resolution enhancement","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Sampling (signal processing); Computer vision; Resolution (logic); Binary number; Pattern recognition (psychology); Mathematics","score_opus":0.12751058433254048,"score_gpt":0.34233961378924566,"score_spread":0.21482902945670518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089058832","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028109926,0.00002659888,0.99782676,0.00009778406,0.00005418467,0.0002319067,0.0000075651137,0.0004384355,0.001035657],"genre_scores_gemma":[0.23563333,0.000001122841,0.76352847,0.00057001616,0.000017282346,0.000066035114,0.0000075311004,0.000006381855,0.0001698345],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909645,0.0000082403685,0.00019977111,0.00032516065,0.00015374892,0.00021662608],"domain_scores_gemma":[0.9993557,0.000028203947,0.000090701986,0.00029375197,0.00018742243,0.00004426274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001251948,0.000104282866,0.00009167245,0.000067905305,0.00014040909,0.00004943157,0.00044904993,0.00003317368,0.000032625507],"category_scores_gemma":[0.00005668702,0.00009607165,0.00003534681,0.000097434,0.00003756718,0.000747523,0.00013691097,0.00005646803,0.0000033038045],"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":[0.000030675787,0.00009145758,0.000017115737,0.000050520594,0.000018526733,0.0000011770856,0.0010134049,0.0012142573,0.16144261,0.818889,0.0054820664,0.011749156],"study_design_scores_gemma":[0.00008725239,0.000021544403,0.000013632668,0.00000675095,0.000001558872,0.0000034739312,0.0000016756393,0.50360197,0.08325754,0.4128236,0.00010588875,0.00007511748],"about_ca_topic_score_codex":0.0000044857998,"about_ca_topic_score_gemma":0.0000030177598,"teacher_disagreement_score":0.5023877,"about_ca_system_score_codex":0.00006421973,"about_ca_system_score_gemma":0.00007443346,"threshold_uncertainty_score":0.3917688},"labels":[],"label_agreement":null},{"id":"W2090313827","doi":"10.1109/icassp.2010.5495316","title":"Temporal motion smoothness measurement for reduced-reference video quality assessment","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Video quality; Lossy compression; Reference frame; Motion compensation; Rate–distortion optimization; Distortion (music); Data compression; Motion estimation; Block-matching algorithm; Video compression picture types; Wavelet; Smoothness; Frame (networking); Noise (video); Video tracking; Video processing; Mathematics; Bandwidth (computing); Metric (unit)","score_opus":0.11911707133618574,"score_gpt":0.3911745354043095,"score_spread":0.2720574640681238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090313827","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029733095,0.000011130664,0.9907194,0.0015428521,0.00037108577,0.00041146638,0.0000022441227,0.0009432234,0.0030253136],"genre_scores_gemma":[0.4800719,6.9555324e-7,0.5195701,0.00012483295,0.000030160483,0.00011816408,0.0000024178332,0.0000067236183,0.00007496447],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981591,0.00005696361,0.00035794615,0.0005572467,0.0005729257,0.00029582664],"domain_scores_gemma":[0.9981297,0.000060020746,0.0001923327,0.0008122347,0.00071109395,0.00009456764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017752026,0.00017152801,0.00018529984,0.000074948985,0.00018747569,0.00023774433,0.0009591056,0.00008466846,0.000014839075],"category_scores_gemma":[0.00026524306,0.00014844627,0.000055328765,0.0001973726,0.000059678292,0.0010797337,0.0002135824,0.00024871016,0.0000055214214],"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":[0.000008328596,0.00021177612,0.001070562,0.000074605676,0.000009131889,7.291619e-7,0.00007832828,0.0000026733082,0.5837841,0.21390325,0.0010466864,0.19980983],"study_design_scores_gemma":[0.0009879592,0.00025330635,0.024592979,0.00007592922,0.000014301917,0.000011818771,0.000041489795,0.062474944,0.4518405,0.44361424,0.0150393,0.0010532532],"about_ca_topic_score_codex":0.000061490835,"about_ca_topic_score_gemma":0.000075054566,"teacher_disagreement_score":0.4770986,"about_ca_system_score_codex":0.00011264725,"about_ca_system_score_gemma":0.0002156974,"threshold_uncertainty_score":0.6053463},"labels":[],"label_agreement":null},{"id":"W2091385987","doi":"10.1109/crv.2010.51","title":"Mammogram Image Superresolution Based on Statistical Moment Analysis","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Kurtosis; Computer science; Pixel; Moment (physics); Image resolution; Skewness; Artificial intelligence; Computer vision; Resolution (logic); Pattern recognition (psychology); Mathematics; Statistics; Physics","score_opus":0.0061911302190580395,"score_gpt":0.2850526356530898,"score_spread":0.27886150543403176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091385987","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038829853,0.0000022015347,0.99400824,0.00092271215,0.00007060543,0.00008371507,0.000004107878,0.0007580673,0.0037620733],"genre_scores_gemma":[0.30429047,4.093307e-7,0.69525445,0.00032455643,0.0000121006005,0.000018122146,0.0000073880656,0.0000046155565,0.00008785404],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998875,0.000030411169,0.00015713213,0.0003884053,0.00030686273,0.00024217581],"domain_scores_gemma":[0.99902177,0.00009062963,0.000037631646,0.000660767,0.000090396126,0.00009879345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023342985,0.00011623151,0.0001322169,0.0002622667,0.00009312732,0.00020087147,0.0005282446,0.000044900775,0.00014931663],"category_scores_gemma":[0.00008247011,0.00009777803,0.000058697555,0.0007494613,0.00008452885,0.0003712754,0.000074583295,0.00020856182,0.00004287511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039340313,0.0010721863,0.0034314587,0.000033987562,0.0001094002,0.00009261202,0.00012577428,0.00043351317,0.1915642,0.48839083,0.007506625,0.30720007],"study_design_scores_gemma":[0.000101037476,0.000073956486,0.0024821565,0.0000023180735,0.000023340097,0.0000012383408,0.0000016100425,0.9723909,0.014737544,0.008614417,0.0014241957,0.0001472978],"about_ca_topic_score_codex":0.00002437628,"about_ca_topic_score_gemma":0.000024085415,"teacher_disagreement_score":0.9719574,"about_ca_system_score_codex":0.000032405325,"about_ca_system_score_gemma":0.000039684975,"threshold_uncertainty_score":0.39872724},"labels":[],"label_agreement":null},{"id":"W2091588468","doi":"10.1109/icip.2012.6467151","title":"Objective quality assessment for image super-resolution: A natural scene statistics approach","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpolation (computer graphics); Image resolution; Computer science; Image quality; Image (mathematics); Artificial intelligence; Resolution (logic); Quality (philosophy); Scene statistics; Computer vision; Image scaling; Algorithm; Data mining; Pattern recognition (psychology); Image processing","score_opus":0.04388624292770592,"score_gpt":0.3828827118039825,"score_spread":0.33899646887627655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091588468","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000057658617,0.00018309144,0.9945198,0.0002451757,0.00021907539,0.00040145905,0.000024540062,0.0006273123,0.0037218651],"genre_scores_gemma":[0.12907779,0.0000029338435,0.87004304,0.0002419587,0.000095739095,0.00013965646,0.0000197987,0.000011934564,0.00036715847],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985102,0.00009443865,0.00025835008,0.000361881,0.00029360014,0.00048152797],"domain_scores_gemma":[0.9987521,0.000199281,0.00010669442,0.0004715286,0.0003659233,0.00010444153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007591276,0.00016942501,0.00019491653,0.000061618885,0.00021274608,0.0001689896,0.00056707475,0.00004815535,0.000005970032],"category_scores_gemma":[0.00019274524,0.00014546321,0.00005130822,0.00022387055,0.00009935675,0.001886117,0.00029646396,0.00017632793,0.000004763214],"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":[0.000024170538,0.000681158,0.0008061242,0.00020256524,0.00003833798,0.0000015684017,0.0010537957,0.00000525938,0.016616594,0.8903347,0.011092546,0.07914318],"study_design_scores_gemma":[0.0009217779,0.00018269937,0.010775337,0.000023611747,0.000021468215,0.000047225712,0.00025690385,0.8532623,0.020877328,0.110065214,0.0026631423,0.00090296526],"about_ca_topic_score_codex":0.000021039641,"about_ca_topic_score_gemma":0.000001819577,"teacher_disagreement_score":0.85325706,"about_ca_system_score_codex":0.00020495775,"about_ca_system_score_gemma":0.00011576711,"threshold_uncertainty_score":0.5931818},"labels":[],"label_agreement":null},{"id":"W2093856046","doi":"10.1109/tip.2011.2160188","title":"Preconditioning for Edge-Preserving Image Super Resolution","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Pixel; Hessian matrix; Preconditioner; Computer science; Algorithm; Image resolution; Mathematical optimization; Mathematics; Iterative method; Artificial intelligence; Applied mathematics","score_opus":0.037852813120643765,"score_gpt":0.28894107532873375,"score_spread":0.25108826220809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093856046","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030043873,0.00015778825,0.99370825,0.00021716612,0.00029617012,0.00045107098,0.0000143030475,0.0016086798,0.0032461155],"genre_scores_gemma":[0.21228783,0.000012407668,0.7867284,0.00017837287,0.000052530417,0.00035469944,0.0000026256114,0.00005191314,0.00033124047],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997849,0.000059649017,0.00044331787,0.00078093034,0.00027924974,0.0005878529],"domain_scores_gemma":[0.9984259,0.00010023716,0.00019767087,0.0006331627,0.0005146422,0.00012838261],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039907952,0.00031611204,0.00024905268,0.00037029386,0.0010514614,0.000527956,0.0009978061,0.00012277596,0.00006314432],"category_scores_gemma":[0.000053872624,0.00033738537,0.00014511652,0.0005865605,0.0001817338,0.0064213085,0.0000138371715,0.00037921892,0.000032871558],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022700359,0.00096838275,0.000010208002,0.0010695993,0.000070169444,0.000026290976,0.0097186165,0.0003359913,0.38847142,0.0007270916,0.0016043859,0.5967708],"study_design_scores_gemma":[0.00059294445,0.0001882507,0.000042940388,0.0004086074,0.000040934156,0.00005920531,0.00014998164,0.26503226,0.7103153,0.022270871,0.00029547865,0.00060320785],"about_ca_topic_score_codex":0.000015227486,"about_ca_topic_score_gemma":0.0000061571673,"teacher_disagreement_score":0.5961676,"about_ca_system_score_codex":0.0001432811,"about_ca_system_score_gemma":0.00015604305,"threshold_uncertainty_score":0.9999078},"labels":[],"label_agreement":null},{"id":"W2093909263","doi":"10.1109/sitis.2013.43","title":"Motion Estimation in Blurred Frames Using Phase Correlation","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Motion estimation; Phase correlation; Motion vector; Computer vision; Artificial intelligence; Computer science; Quarter-pixel motion; Motion blur; Correlation; Motion (physics); Noise (video); Phase (matter); Mathematics; Image (mathematics); Physics; Fourier transform","score_opus":0.022217135130744966,"score_gpt":0.3279127560648183,"score_spread":0.3056956209340733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093909263","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024759209,0.0000197909,0.97405183,0.00021619315,0.000062111234,0.00018017988,8.265332e-8,0.0004344205,0.00027618566],"genre_scores_gemma":[0.45414317,6.220813e-7,0.5457649,0.000044800385,0.0000037250884,0.000009640134,0.0000012682978,0.0000027102901,0.00002912036],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999396,0.000022828828,0.00016575147,0.00018582189,0.00011137541,0.00011821021],"domain_scores_gemma":[0.99960417,0.000026994769,0.00007482881,0.00019392051,0.00007597824,0.000024135099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009672383,0.00006853722,0.0000659783,0.00014010511,0.00004516047,0.00015036613,0.00018923708,0.00004193948,0.000021348085],"category_scores_gemma":[0.00009112843,0.000066111366,0.000011775839,0.0003252012,0.000017656432,0.0030645453,0.00005992553,0.0000770145,0.00003425303],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015437478,0.00012338402,0.00060323195,0.000011117563,0.0000012180647,0.00000141067,0.00037075937,0.0064282725,0.025963616,0.007078485,0.00013049485,0.95928645],"study_design_scores_gemma":[0.00018484014,0.000017086897,0.00045306975,0.000022209877,6.9564356e-7,0.0000037580824,0.000007847183,0.9385626,0.006469327,0.05419481,0.000009345329,0.00007443765],"about_ca_topic_score_codex":0.00006496015,"about_ca_topic_score_gemma":0.0000013009129,"teacher_disagreement_score":0.959212,"about_ca_system_score_codex":0.00006820174,"about_ca_system_score_gemma":0.000018855311,"threshold_uncertainty_score":0.26959434},"labels":[],"label_agreement":null},{"id":"W2094818091","doi":"10.1109/icip.2014.7026100","title":"Multi-view video super-resolution for hybrid cameras using modified NLM and adaptive thresholding","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial intelligence; Thresholding; Computer vision; Pixel; Image resolution; Frame (networking); Frame rate; Exploit; Display resolution; Fidelity; Image (mathematics)","score_opus":0.06347298899962533,"score_gpt":0.31448760322108743,"score_spread":0.2510146142214621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094818091","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034029149,0.00054130977,0.9947688,0.00021026042,0.000072825256,0.00031990875,0.0000017612598,0.00051571557,0.00016647925],"genre_scores_gemma":[0.37613726,0.00001191473,0.62351614,0.00023537542,0.000025953193,0.000023834446,7.047701e-7,0.000010996081,0.000037803886],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881965,0.000040989606,0.00020625218,0.0004908224,0.0001343008,0.0003080033],"domain_scores_gemma":[0.9992112,0.00012751861,0.0000852094,0.00034315468,0.00015807731,0.0000748519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037633153,0.00016711245,0.00019972767,0.00009317823,0.00026622324,0.00017476155,0.00037916139,0.00003901225,0.0000015056456],"category_scores_gemma":[0.00015527663,0.00015200334,0.000040869083,0.00013775055,0.00008170963,0.0011874923,0.00026504722,0.00009325846,0.000001378291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007489716,0.00021333656,0.00024490515,0.00030322894,0.000050569543,0.000011129158,0.0010591015,0.0032592635,0.23810063,0.17578244,0.001090956,0.57980955],"study_design_scores_gemma":[0.00030561996,0.00006620572,0.000029146368,0.00006946649,0.0000070548676,0.000024982834,0.000011190419,0.9591129,0.020243607,0.01942162,0.0005022383,0.00020596254],"about_ca_topic_score_codex":0.00006100058,"about_ca_topic_score_gemma":0.000005927108,"teacher_disagreement_score":0.95585364,"about_ca_system_score_codex":0.00005205899,"about_ca_system_score_gemma":0.00003497515,"threshold_uncertainty_score":0.6198516},"labels":[],"label_agreement":null},{"id":"W2095097143","doi":"10.1109/pacrim.2011.6033016","title":"Spatio-temporal super-resolution from compressed video employing global and local motion","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Residual; Interpolation (computer graphics); Computer vision; Block (permutation group theory); Artificial intelligence; Motion estimation; Block-matching algorithm; Frame (networking); Motion (physics); Motion compensation; Image resolution; Compressed sensing; Reference frame; Algorithm; Mathematics; Video processing; Video tracking; Telecommunications","score_opus":0.03410687146937102,"score_gpt":0.2632673101961902,"score_spread":0.22916043872681918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095097143","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01882497,0.00013955182,0.97866523,0.00013864216,0.00008644117,0.000103264865,0.0000036634253,0.0009389936,0.001099242],"genre_scores_gemma":[0.5217489,0.0000025163906,0.47811344,0.00010026338,0.000012690379,0.000005118753,0.000005243224,0.0000036429403,0.000008178656],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899507,0.00004489812,0.0001887185,0.0003978167,0.00017470149,0.00019880486],"domain_scores_gemma":[0.99941593,0.000021776412,0.00006497595,0.00033126507,0.00008886229,0.00007718206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102823666,0.00013056542,0.0001249319,0.00003743284,0.00011969129,0.000109672874,0.00039657307,0.000063666535,0.000022517908],"category_scores_gemma":[0.000025159487,0.00012352943,0.000022724626,0.00017478029,0.00011008319,0.0014158137,0.00031011194,0.00007879284,0.0000131109755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011808431,0.0004050216,0.22737238,0.00006115875,0.00003520903,0.000060022674,0.0024944996,0.000077413555,0.005643408,0.07230193,0.0027566988,0.6886742],"study_design_scores_gemma":[0.00043129816,0.000103048136,0.057989582,0.00005413843,0.000008713667,0.000017380093,0.000041778618,0.737506,0.018658778,0.18436678,0.00046717835,0.00035533533],"about_ca_topic_score_codex":0.001604982,"about_ca_topic_score_gemma":0.00014707669,"teacher_disagreement_score":0.7374286,"about_ca_system_score_codex":0.000066781904,"about_ca_system_score_gemma":0.000025301395,"threshold_uncertainty_score":0.5037384},"labels":[],"label_agreement":null},{"id":"W2097790247","doi":"10.5220/0002933401030107","title":"FAST EDGE-GUIDED INTERPOLATION OF COLOR IMAGES","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Demosaicing; Stairstep interpolation; Computer vision; Computer science; Artificial intelligence; Bicubic interpolation; Interpolation (computer graphics); Bilinear interpolation; Image scaling; Luminance; Linear interpolation; Chrominance; Enhanced Data Rates for GSM Evolution; Nearest-neighbor interpolation; Channel (broadcasting); Ringing artifacts; Color image; Multivariate interpolation; Image (mathematics); Image processing; Pattern recognition (psychology)","score_opus":0.012823314408574763,"score_gpt":0.2990064919517461,"score_spread":0.28618317754317135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097790247","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003919053,0.000008861875,0.98187816,0.00041465572,0.00014786162,0.00007017621,5.4500015e-7,0.00043924357,0.013121454],"genre_scores_gemma":[0.36941466,8.6284524e-7,0.6301323,0.00007540486,0.000013568628,0.0000046425653,3.4671484e-7,0.000003331236,0.00035488277],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99943995,0.000009957134,0.00016552742,0.0001737028,0.00010558691,0.00010524828],"domain_scores_gemma":[0.99930406,0.00003800046,0.00009484922,0.00035825418,0.00017534067,0.000029489513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013332097,0.00006733341,0.000087124,0.0000760683,0.000033726807,0.000055094933,0.00058289355,0.000035978752,0.000028702405],"category_scores_gemma":[0.000120976736,0.000057375335,0.000024118908,0.00018037959,0.00007538002,0.0007784198,0.00022050795,0.000108916,0.000011858597],"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":[0.0000014013127,0.000025237956,0.0003066525,0.000009343986,0.0000017493373,7.6216634e-7,0.00011063436,9.259572e-7,0.93405974,0.024055067,0.0027261258,0.03870233],"study_design_scores_gemma":[0.00011161316,0.000049282768,0.001028558,0.000014629941,0.0000017480575,0.000015267486,0.00001049929,0.10142964,0.86594796,0.030265456,0.0010013093,0.00012401427],"about_ca_topic_score_codex":0.00000978389,"about_ca_topic_score_gemma":0.000005114558,"teacher_disagreement_score":0.3654956,"about_ca_system_score_codex":0.000006316565,"about_ca_system_score_gemma":0.000033356177,"threshold_uncertainty_score":0.23396982},"labels":[],"label_agreement":null},{"id":"W2098535678","doi":"10.1145/1141911.1141956","title":"Removing camera shake from a single photograph","year":2006,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1842,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Shake; Computer vision; Artificial intelligence; Camera auto-calibration; Computer science; Motion blur; Photography; Deconvolution; Digital camera; Blind deconvolution; Computer graphics (images); Rotation (mathematics); Image restoration; Pinhole camera model; Image (mathematics); Camera resectioning; Image processing; Physics; Algorithm; Art","score_opus":0.0171727493652327,"score_gpt":0.24398589957926964,"score_spread":0.22681315021403695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098535678","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046841805,0.00025483978,0.991901,0.00095359044,0.00018938928,0.00013381729,0.000017526603,0.0014363619,0.0004293177],"genre_scores_gemma":[0.3922914,0.000045826477,0.60695904,0.0005744649,0.00002698082,0.00002935761,0.0000045222805,0.000020625257,0.000047801874],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99843997,0.000058474314,0.0002781551,0.00056634477,0.0003283039,0.00032872605],"domain_scores_gemma":[0.9981317,0.00026969172,0.000105414096,0.0013077423,0.00011497844,0.00007045508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014816994,0.00022325825,0.0001783682,0.0004081389,0.00037184477,0.0002268194,0.0012486998,0.00011663499,0.000017756456],"category_scores_gemma":[0.00003657383,0.00023527078,0.00015641593,0.0013722845,0.00013264861,0.00078628474,0.000022178749,0.00038813628,0.000012181317],"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":[0.00008884196,0.002366353,0.00096587476,0.00006500794,0.00014225904,0.00013862083,0.0008360384,0.0006683096,0.23750427,0.0101726595,0.0017453954,0.7453064],"study_design_scores_gemma":[0.0008576253,0.0003064087,0.0024166575,0.00028278757,0.00007127314,0.000055630473,0.000052029925,0.036529936,0.19664104,0.7480561,0.013456221,0.0012742699],"about_ca_topic_score_codex":0.00040214197,"about_ca_topic_score_gemma":0.00023617489,"teacher_disagreement_score":0.7440321,"about_ca_system_score_codex":0.000043427648,"about_ca_system_score_gemma":0.00003964242,"threshold_uncertainty_score":0.95940644},"labels":[],"label_agreement":null},{"id":"W2099969223","doi":"10.1109/83.821737","title":"Robust classification of blurred imagery","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Toronto","funders":"","keywords":"Artificial intelligence; Image restoration; Image fusion; Computer science; Computer vision; Invariant (physics); Contextual image classification; Markov random field; Pattern recognition (psychology); Fusion; Image (mathematics); Image processing; Mathematics; Algorithm; Image segmentation","score_opus":0.032879990273089264,"score_gpt":0.280296187092602,"score_spread":0.24741619681951274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099969223","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011485115,0.0001509206,0.99388486,0.00041738542,0.000091476584,0.00016758536,0.0000054275188,0.00090616284,0.0032276735],"genre_scores_gemma":[0.4532746,0.000039054026,0.54610807,0.00008817773,0.000016400814,0.0000414514,8.270589e-7,0.000022177968,0.0004092116],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982399,0.000055460434,0.0004612225,0.00055597664,0.0003629739,0.00032450282],"domain_scores_gemma":[0.99876297,0.00006216752,0.00019693743,0.00061356457,0.0002790087,0.00008536704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002258407,0.00023341282,0.0002417091,0.00027348415,0.00031959222,0.00025120954,0.00075378583,0.00008811985,0.00010265442],"category_scores_gemma":[0.000011599084,0.00023888271,0.00010125193,0.0009430626,0.00021868836,0.0027896275,0.000002927371,0.0003174717,0.000055327717],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025211153,0.00030436623,0.0000017835682,0.00009800234,0.0000068483555,0.000004936171,0.00030790703,0.0007356317,0.13211903,0.000034086355,0.0000796082,0.8662826],"study_design_scores_gemma":[0.00041151277,0.00012576995,0.00009704468,0.00031007663,0.000026851665,0.00005256447,0.000047036337,0.4778445,0.51714283,0.0031436277,0.0003572441,0.00044090574],"about_ca_topic_score_codex":0.0000058197193,"about_ca_topic_score_gemma":0.0000012195336,"teacher_disagreement_score":0.8658417,"about_ca_system_score_codex":0.00007533719,"about_ca_system_score_gemma":0.00015652324,"threshold_uncertainty_score":0.9741354},"labels":[],"label_agreement":null},{"id":"W2101598492","doi":"10.1109/crv.2006.23","title":"Comparison of Super-Resolution Algorithms Using Image Quality Measures","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Interpolation (computer graphics); Computer science; Image (mathematics); Artificial intelligence; Image quality; Measure (data warehouse); Image resolution; Resolution (logic); Quality (philosophy); Algorithm; Superresolution; Image scaling; Pattern recognition (psychology); Computer vision; Image processing; Data mining","score_opus":0.09217231104861004,"score_gpt":0.414465494150966,"score_spread":0.322293183102356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101598492","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059238803,0.00026476744,0.9909354,0.00012237445,0.00005621579,0.000085451626,0.0000013943161,0.0005264688,0.0020840846],"genre_scores_gemma":[0.38921335,7.978846e-7,0.6107026,0.000017072709,0.00001956083,0.0000019563656,0.0000010230511,0.0000047173294,0.000038958053],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863225,0.0000816779,0.00042301766,0.00028697107,0.000361627,0.00021446969],"domain_scores_gemma":[0.99903834,0.000048597714,0.00017190611,0.0004318704,0.00027947675,0.000029826746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041804803,0.00011691141,0.00023105818,0.00009085783,0.00010196567,0.000082150946,0.0005380198,0.000049244936,0.0000048439338],"category_scores_gemma":[0.000063078754,0.000108136875,0.000052295967,0.00032404254,0.00011823851,0.00094872445,0.00018856191,0.000093200615,0.0000030153767],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006399438,0.00029848356,0.0052953367,0.000049999537,0.0000060221123,0.0000020711143,0.00020354243,0.00036674354,0.9276736,0.028877148,0.001012923,0.036207758],"study_design_scores_gemma":[0.000111772744,0.000026551716,0.0019704532,0.000019479061,0.0000033993535,0.000004004507,0.00002151297,0.52208334,0.4542115,0.021134306,0.0002537279,0.00015993245],"about_ca_topic_score_codex":0.0007533288,"about_ca_topic_score_gemma":0.00002455996,"teacher_disagreement_score":0.5217166,"about_ca_system_score_codex":0.00006467461,"about_ca_system_score_gemma":0.00005128803,"threshold_uncertainty_score":0.44096938},"labels":[],"label_agreement":null},{"id":"W2103882108","doi":"10.1186/1687-6180-2012-16","title":"SSIM-inspired image restoration using sparse representation","year":2012,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sparse approximation; Metric (unit); Image quality; Mean squared error; Computer science; Norm (philosophy); Gradient descent; Representation (politics); Artificial intelligence; Pattern recognition (psychology); Image restoration; Algorithm; Peak signal-to-noise ratio; Image (mathematics); Mathematics; Image processing; Statistics; Artificial neural network","score_opus":0.04963724024173807,"score_gpt":0.36971601822226563,"score_spread":0.32007877798052753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103882108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013261044,0.0060951733,0.978242,0.00023846909,0.00037575763,0.00015803729,5.297088e-7,0.0002713155,0.0013577151],"genre_scores_gemma":[0.5183977,0.00014374117,0.48087993,0.00021023871,0.0003203028,0.00000643389,9.0195414e-7,0.000024151628,0.000016630569],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99718064,0.00025834457,0.00073433155,0.0004332424,0.00072856835,0.0006648847],"domain_scores_gemma":[0.9982167,0.00012501104,0.00084191124,0.0003090298,0.00029742814,0.00020991475],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011224566,0.0002983233,0.00030053878,0.00051844184,0.0005103322,0.0006372527,0.0007584779,0.00008365638,0.000011347393],"category_scores_gemma":[0.0002932962,0.00027824505,0.00007259588,0.0011726207,0.00012250441,0.019115757,0.00013289468,0.0007226226,0.000014141867],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015076394,0.00040187096,0.0072825956,0.000115029026,0.0000072272765,0.00016619885,0.0019287774,0.0055327844,0.12813571,0.0015230111,0.00009405361,0.854662],"study_design_scores_gemma":[0.0025434343,0.000621626,0.004113855,0.0033292868,0.000041621195,0.0023529946,0.000702908,0.7080686,0.17518483,0.09256894,0.008338985,0.0021329238],"about_ca_topic_score_codex":0.0000014737576,"about_ca_topic_score_gemma":9.2008037e-7,"teacher_disagreement_score":0.85252905,"about_ca_system_score_codex":0.00040518373,"about_ca_system_score_gemma":0.00016834572,"threshold_uncertainty_score":0.999967},"labels":[],"label_agreement":null},{"id":"W2106970482","doi":"10.1109/tip.2008.2008046","title":"Decorrelating the Structure and Texture Components of a Variational Decomposition Model","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Decorrelation; Decomposition; Texture (cosmology); Computer science; Artificial intelligence; Mathematics; Lambda; Algorithm; Pattern recognition (psychology); Applied mathematics; Image (mathematics); Physics","score_opus":0.01685590721802325,"score_gpt":0.27461047583157966,"score_spread":0.2577545686135564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106970482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010531095,0.00012799882,0.98858804,0.00026847154,0.000056823385,0.00013747632,0.000010770552,0.00021057577,0.00006874501],"genre_scores_gemma":[0.53298986,0.000007327026,0.46684188,0.00012574378,0.000007961324,0.00000632019,8.8621016e-7,0.000008780209,0.00001125075],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889755,0.000040681112,0.00027264218,0.00031663507,0.00029347834,0.00017898665],"domain_scores_gemma":[0.9992139,0.00009193057,0.00020037705,0.00024365827,0.00020412245,0.00004605552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010369812,0.0001671242,0.00015261944,0.00012825233,0.00080550084,0.000105930965,0.0003719441,0.00007080144,0.0000032718683],"category_scores_gemma":[0.00001034202,0.00013280431,0.00004306582,0.00034505577,0.00018763532,0.0014056434,0.000007797279,0.00034140216,8.569051e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000752274,0.00032544014,0.000103497514,0.00024975796,0.000045773264,0.00001509771,0.007168437,0.07689105,0.65062267,0.0006827188,0.000060303573,0.26376],"study_design_scores_gemma":[0.0002056359,0.00002500918,0.00018377202,0.00009793697,0.000012625935,0.00017568607,0.000012040237,0.9386233,0.051523544,0.009001383,0.0000034360364,0.00013562274],"about_ca_topic_score_codex":0.000005295133,"about_ca_topic_score_gemma":0.0000013816096,"teacher_disagreement_score":0.86173224,"about_ca_system_score_codex":0.00004077564,"about_ca_system_score_gemma":0.00009735901,"threshold_uncertainty_score":0.61953425},"labels":[],"label_agreement":null},{"id":"W2107780891","doi":"10.1051/0004-6361:20054614","title":"Estimation of a super-resolved PSF for the data reduction of undersampled stellar observations","year":2006,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Centre National d’Etudes Spatiales","keywords":"Point spread function; Photometry (optics); Data reduction; Computer science; Context (archaeology); Algorithm; Physics; Astrophysics; Artificial intelligence; Stars; Data mining; Geology","score_opus":0.04522273077276544,"score_gpt":0.2748793352347519,"score_spread":0.22965660446198644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107780891","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077407937,0.00006243039,0.99166065,0.000249568,0.000028430688,0.00018766106,0.00002809335,0.00003568101,0.000006663684],"genre_scores_gemma":[0.3978183,0.0000013751993,0.6020951,0.0000020991436,0.000024193458,0.000007913082,0.00004331024,0.0000030865185,0.0000046336727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994634,0.000012384527,0.00017971684,0.0001746765,0.00007727983,0.00009258954],"domain_scores_gemma":[0.99917203,0.00008910212,0.00015575251,0.00048990594,0.00008251146,0.000010668928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008567816,0.000069289985,0.00009987681,0.000022442428,0.00010223074,0.000033549437,0.0004388443,0.000015516745,2.8710275e-7],"category_scores_gemma":[0.000013455314,0.000058177946,0.00002242111,0.00013974379,0.00009480858,0.00071832817,0.00015688116,0.000041432955,8.959397e-8],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000128728025,0.00008619222,0.000106014486,0.000036150166,0.000020479883,3.1482852e-8,0.0000960937,0.0084338,0.023645608,0.03666674,0.00018309997,0.93071294],"study_design_scores_gemma":[0.00053392857,0.0001891512,0.001846511,0.000057449823,0.00006467671,0.0000018421296,0.00020460351,0.8676927,0.07114509,0.0550018,0.0030929004,0.00016932932],"about_ca_topic_score_codex":0.000039593793,"about_ca_topic_score_gemma":0.0000012307104,"teacher_disagreement_score":0.9305436,"about_ca_system_score_codex":0.0000082847655,"about_ca_system_score_gemma":0.000045911573,"threshold_uncertainty_score":0.23724279},"labels":[],"label_agreement":null},{"id":"W2109123947","doi":"10.1109/icip.2010.5652738","title":"High frame rate video capture by multiple cameras with coded exposure","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Hfr cell; Video capture; Frame rate; Artificial intelligence; Computer vision; Frame (networking); Image resolution; Video processing; Telecommunications","score_opus":0.004237554536542646,"score_gpt":0.21801788663614574,"score_spread":0.2137803320996031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109123947","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025502542,0.000059430164,0.97048676,0.0017303724,0.0001554371,0.00017547166,0.0000052971886,0.0014641827,0.00042052392],"genre_scores_gemma":[0.46157327,0.0000021845256,0.5364142,0.0010689257,0.000022155164,0.000025320594,0.0000036136785,0.00001428411,0.0008760046],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99871737,0.000032333857,0.00017411752,0.0005232889,0.00022331797,0.0003296036],"domain_scores_gemma":[0.9986463,0.00011022772,0.000111296096,0.0008180211,0.0001932849,0.00012086086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015993477,0.0002186872,0.00019279482,0.000053892094,0.00013955245,0.00024967027,0.0009855868,0.00012686377,0.00003956293],"category_scores_gemma":[0.000116405834,0.00015815832,0.000026826436,0.00029869424,0.00012769204,0.0010258831,0.00019095368,0.00051648566,0.000027768072],"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":[0.000025149804,0.00010783589,0.0018481504,0.00001904383,0.000016986809,0.00003309779,0.00066652655,0.000013672021,0.9545365,0.007473413,0.025231224,0.0100283995],"study_design_scores_gemma":[0.0012485671,0.00027529284,0.000631531,0.000045807494,0.000010732906,0.000079238554,0.00004279991,0.047937214,0.9038101,0.019695226,0.025276978,0.0009465007],"about_ca_topic_score_codex":0.00018503188,"about_ca_topic_score_gemma":0.0001580009,"teacher_disagreement_score":0.43607074,"about_ca_system_score_codex":0.000016792135,"about_ca_system_score_gemma":0.00006902916,"threshold_uncertainty_score":0.6449509},"labels":[],"label_agreement":null},{"id":"W2109200779","doi":"10.1109/icip.2002.1038982","title":"A new direction adaptive scheme for image interpolation","year":2003,"lang":"en","type":"article","venue":"Proceedings - International Conference on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Interpolation (computer graphics); Computer science; Scheme (mathematics); Computer vision; Artificial intelligence; Image scaling; Image (mathematics); Algorithm; Image processing; Mathematics","score_opus":0.04325412093976623,"score_gpt":0.3317851979211418,"score_spread":0.2885310769813756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109200779","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001765198,0.00007440954,0.9265747,0.0014661825,0.00031685355,0.0004205475,0.0000042479965,0.0008665653,0.07009994],"genre_scores_gemma":[0.20327868,0.000017457676,0.795129,0.0002596586,0.0001449673,0.00016924842,0.0000058720243,0.000038662194,0.0009564428],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755114,0.000015808344,0.00047957318,0.0009368049,0.0005621399,0.00045454953],"domain_scores_gemma":[0.9968897,0.000053710224,0.00052434014,0.00017925713,0.0021904975,0.00016247047],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00044973864,0.0003813264,0.00026671748,0.00042763815,0.00033095,0.001768572,0.0011033339,0.00011657663,0.00006797939],"category_scores_gemma":[0.00092891813,0.00039144058,0.00010513292,0.00049929705,0.00009742706,0.0059615714,0.00016197393,0.00035096175,0.00003044662],"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":[0.0001400602,0.00013741691,0.000102811304,0.00010913592,0.000038184862,0.0000034995903,0.0011723536,5.73425e-7,0.3583934,0.5146778,0.0028451257,0.12237963],"study_design_scores_gemma":[0.00097530585,0.0002784411,0.00005839233,0.00065290916,0.000016914653,0.00007803746,0.0003848453,0.553382,0.14111294,0.29866204,0.0036544027,0.0007437991],"about_ca_topic_score_codex":0.0000073204524,"about_ca_topic_score_gemma":0.0000011141483,"teacher_disagreement_score":0.55338144,"about_ca_system_score_codex":0.0002501539,"about_ca_system_score_gemma":0.00038055077,"threshold_uncertainty_score":0.99985373},"labels":[],"label_agreement":null},{"id":"W2109244936","doi":"10.1109/tip.2011.2134106","title":"Lookup-Table-Based Gradient Field Reconstruction","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Engineering and Physical Sciences Research Council","keywords":"Lookup table; Algorithm; Field (mathematics); Computer science; Iterative reconstruction; Image gradient; Vector field; Scalar field; Mathematics; Artificial intelligence; Computer vision; Image (mathematics); Image processing; Geometry; Edge detection","score_opus":0.025580949451477434,"score_gpt":0.25842021216079897,"score_spread":0.23283926270932154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109244936","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003549751,0.0001035591,0.9938936,0.0002548301,0.0004337462,0.00018804705,0.000002416916,0.0014682951,0.0033005176],"genre_scores_gemma":[0.36705098,0.000009193987,0.63230646,0.0004225806,0.000018870185,0.00006836449,2.9663045e-7,0.000022445785,0.00010082342],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982691,0.00004962016,0.0003553041,0.00063061976,0.00027279346,0.0004225826],"domain_scores_gemma":[0.99880445,0.000059971317,0.00018820817,0.00056052714,0.00026200144,0.00012481955],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020352476,0.0002706554,0.00020620215,0.00035174383,0.0005222761,0.00027823757,0.0006801126,0.00010944276,0.000070498994],"category_scores_gemma":[0.000018628696,0.0002729616,0.000092090246,0.0007783284,0.00013907452,0.0024991534,0.0000046027208,0.0004265121,0.00003660771],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047868914,0.00024193531,0.000010968874,0.000097501055,0.000009350662,0.000019377952,0.0005176845,0.00006988602,0.019541215,0.00008832246,0.00006670279,0.9792892],"study_design_scores_gemma":[0.00036682206,0.00021752306,0.000011365591,0.0002752818,0.000021580387,0.000099110104,0.000040030434,0.13333015,0.8586731,0.0064409147,0.00013054717,0.00039356007],"about_ca_topic_score_codex":0.00002658792,"about_ca_topic_score_gemma":0.0000066360217,"teacher_disagreement_score":0.9788956,"about_ca_system_score_codex":0.00010670228,"about_ca_system_score_gemma":0.0002052814,"threshold_uncertainty_score":0.9999723},"labels":[],"label_agreement":null},{"id":"W2111438680","doi":"10.1109/jstsp.2010.2052236","title":"Frame Rate Converter With Pixel-Based Motion Vectors Selection and Halo Reduction Using Preliminary Interpolation","year":2010,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Interpolation (computer graphics); Pixel; Motion vector; Artificial intelligence; Computer science; Computer vision; Halo; Motion estimation; Frame rate; Blocking (statistics); Stairstep interpolation; Block (permutation group theory); Frame (networking); Reduction (mathematics); Mathematics; Multivariate interpolation; Bilinear interpolation; Image (mathematics); Physics","score_opus":0.012832788433345262,"score_gpt":0.26570156128284833,"score_spread":0.25286877284950304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111438680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38647917,0.00005528249,0.61307573,0.00012924559,0.00012760604,0.00007083289,7.5786176e-8,0.000055442266,0.0000066053735],"genre_scores_gemma":[0.65114397,0.0000026699206,0.34864601,0.000025149351,0.00016465619,0.0000018360371,3.6118053e-7,0.000011888116,0.0000034621232],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987373,0.00009707857,0.00044386362,0.0002579591,0.0002510605,0.00021278561],"domain_scores_gemma":[0.9982906,0.000045303987,0.0006251763,0.00009435555,0.00087895704,0.000065613895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044658844,0.00017185321,0.00021152255,0.0004672643,0.00016297106,0.0002601316,0.0002313656,0.00013864071,0.0000027016085],"category_scores_gemma":[0.00006850947,0.00015224497,0.000023486027,0.00088853034,0.000082796665,0.002291279,0.000021083679,0.0008629668,1.5553734e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015971148,0.00008097685,0.0024058193,0.00008246756,0.000008058131,0.000009377877,0.00052910676,0.0009907471,0.92428035,0.0000325084,0.0000048743686,0.071416005],"study_design_scores_gemma":[0.00045410646,0.0004732845,0.0021325352,0.00042871202,0.000019643481,0.00058172585,0.000024738867,0.8112854,0.18155977,0.002845753,0.000012191277,0.00018218879],"about_ca_topic_score_codex":0.0000076232627,"about_ca_topic_score_gemma":0.0000067898045,"teacher_disagreement_score":0.8102946,"about_ca_system_score_codex":0.00011592726,"about_ca_system_score_gemma":0.00037147847,"threshold_uncertainty_score":0.620837},"labels":[],"label_agreement":null},{"id":"W2111843789","doi":"10.1109/icip.2007.4379987","title":"Structure Preserving Image Interpolation via Adaptive 2D Autoregressive Modeling","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Autoregressive model; Pixel; Computer science; Interpolation (computer graphics); Linear interpolation; Artificial intelligence; Image (mathematics); Piecewise linear function; Mathematical optimization; Algorithm; Mathematics; Pattern recognition (psychology); Statistics","score_opus":0.014746884920482133,"score_gpt":0.28833327984035934,"score_spread":0.2735863949198772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111843789","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007942423,0.00009634997,0.9937585,0.00012623628,0.00011034361,0.00014348768,9.1129857e-7,0.0010579254,0.003912021],"genre_scores_gemma":[0.45291793,7.4354017e-7,0.5468743,0.00009381263,0.000036115987,0.0000020755876,0.0000010153835,0.000008912194,0.00006510921],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987297,0.000029441997,0.00025140244,0.00041539792,0.0002530642,0.00032100175],"domain_scores_gemma":[0.9989656,0.00006948037,0.00013114898,0.0004720268,0.00028605876,0.0000756609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029156855,0.00016786251,0.00012900462,0.00015455704,0.00014129911,0.0001734417,0.0008962676,0.00007768015,0.00002459903],"category_scores_gemma":[0.000101638536,0.00014537638,0.000037423146,0.00024666928,0.000039928327,0.0025617278,0.000548009,0.00022982646,0.0000054746115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007571584,0.00007266414,0.00023864766,0.00006432075,0.0000432577,0.000093922376,0.005752235,0.0023253222,0.62132907,0.03898629,0.0008523875,0.33016613],"study_design_scores_gemma":[0.000067422385,0.00002642013,0.000036372545,0.00004286307,0.0000022171469,0.000014929842,0.00004649072,0.859642,0.042011198,0.09793443,0.000017346381,0.0001582596],"about_ca_topic_score_codex":0.000035581124,"about_ca_topic_score_gemma":0.000025653078,"teacher_disagreement_score":0.85731673,"about_ca_system_score_codex":0.00008417941,"about_ca_system_score_gemma":0.000037574846,"threshold_uncertainty_score":0.5928277},"labels":[],"label_agreement":null},{"id":"W2112859965","doi":"10.1007/s10851-010-0250-2","title":"New Possibilities in Image Diffusion and Sharpening via High-Order Sobolev Gradient Flows","year":2011,"lang":"en","type":"article","venue":"Journal of Mathematical Imaging and Vision","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Harvard University; National Science Foundation","keywords":"Sobolev space; Sharpening; Mathematics; Sobolev inequality; Metric (unit); Mathematical analysis; Computer science","score_opus":0.01330371545800931,"score_gpt":0.28882085717625017,"score_spread":0.2755171417182409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112859965","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.123771094,0.00052280707,0.87506944,0.00036948573,0.00004956873,0.000053444062,1.237956e-7,0.00004268987,0.00012132138],"genre_scores_gemma":[0.24535052,0.000043511965,0.75450087,0.00004956604,0.000019262381,6.5721065e-7,6.453605e-8,0.00000805969,0.000027466778],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99888414,0.000043496133,0.00045749714,0.00019887376,0.00022502796,0.0001909435],"domain_scores_gemma":[0.9992508,0.000111006964,0.00020787248,0.0001765828,0.00011408612,0.00013959606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057223754,0.00013542845,0.00026570016,0.00019838012,0.000070278154,0.00018156845,0.00027259436,0.000030171004,0.000013801934],"category_scores_gemma":[0.0001856115,0.00009739482,0.000033085977,0.00014435638,0.000070601935,0.0014367236,0.00028837202,0.00023343472,0.0000018228998],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066958004,0.0005057326,0.0017305597,0.00044608314,0.000015668336,0.00029101115,0.022921437,0.0000018963564,0.115227945,0.011953748,0.00037171962,0.84646726],"study_design_scores_gemma":[0.0006049913,0.00018210888,0.0032055092,0.0010288999,0.000011188028,0.00045462378,0.00016167255,0.18132547,0.0047971937,0.8080341,0.0000140814245,0.00018015325],"about_ca_topic_score_codex":0.000018918061,"about_ca_topic_score_gemma":6.2110234e-7,"teacher_disagreement_score":0.8462871,"about_ca_system_score_codex":0.000024656647,"about_ca_system_score_gemma":0.000028281353,"threshold_uncertainty_score":0.39716455},"labels":[],"label_agreement":null},{"id":"W2114139592","doi":"10.1364/ol.30.000489","title":"Real data results with wavelength-diverse blind deconvolution","year":2005,"lang":"en","type":"article","venue":"Optics Letters","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Optics; Blind deconvolution; Deconvolution; Point spread function; Wavelength; Computer science; Physics","score_opus":0.039756262308544836,"score_gpt":0.2941302997418783,"score_spread":0.2543740374333335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114139592","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0096396515,0.000015299807,0.9775666,0.010416361,0.000069013135,0.0001164959,0.000015502837,0.0004942912,0.0016668087],"genre_scores_gemma":[0.040503,0.000052792286,0.957844,0.0013191832,0.00013602294,0.0000041913963,0.000034878485,0.000013334744,0.00009255722],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867487,0.000024156001,0.00019888008,0.0005516714,0.00026170633,0.0002887011],"domain_scores_gemma":[0.9982163,0.00005283008,0.00013335052,0.0014520234,0.000073192256,0.00007235273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026662878,0.00014115042,0.00010883595,0.000090671056,0.00012560937,0.000168385,0.0015298729,0.000042032694,0.000001442854],"category_scores_gemma":[0.000060889935,0.00012799299,0.000014446918,0.00023984513,0.00010410683,0.0019643959,0.00057297543,0.00017377941,0.000037844813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006505033,0.00045369426,0.00037537861,0.00008031981,0.00013363827,0.0004241638,0.0030434667,0.0026021926,0.08658314,0.017723225,0.11133335,0.77659696],"study_design_scores_gemma":[0.0016425002,0.00011037801,0.000253597,0.00006748534,0.000021730704,0.000068925474,0.000026756019,0.9727599,0.0057558273,0.0003783119,0.018403377,0.00051118777],"about_ca_topic_score_codex":0.000013458008,"about_ca_topic_score_gemma":0.000016127933,"teacher_disagreement_score":0.97015774,"about_ca_system_score_codex":0.000084841144,"about_ca_system_score_gemma":0.000055541663,"threshold_uncertainty_score":0.52194023},"labels":[],"label_agreement":null},{"id":"W2114451991","doi":"10.1109/icassp.1996.547737","title":"Blind image restoration via recursive filtering using deterministic constraints","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Image restoration; Blind deconvolution; Deconvolution; Artificial intelligence; Point spread function; Computer vision; Computer science; Image (mathematics); Object (grammar); Image processing; Algorithm; Mathematics","score_opus":0.057885608052761425,"score_gpt":0.3112027716845978,"score_spread":0.2533171636318364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114451991","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012642434,0.000040500618,0.99367696,0.00014790936,0.00011727601,0.00011990167,7.4982717e-7,0.00050909835,0.0041233893],"genre_scores_gemma":[0.2510818,0.000004035651,0.7486385,0.00013250497,0.000029292012,0.000005361402,4.8016824e-7,0.000008366714,0.000099656216],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990426,0.00003367763,0.00020760269,0.0003301024,0.00015881518,0.00022723315],"domain_scores_gemma":[0.9992589,0.000049897673,0.00011686761,0.00038719704,0.00012442312,0.000062748004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010613053,0.00012652052,0.00011269788,0.000109759705,0.00015240967,0.00023668462,0.00045319268,0.000045403,0.00008067901],"category_scores_gemma":[0.00012283206,0.00012875465,0.000027112012,0.00024669623,0.00015374222,0.0014403604,0.0001671837,0.000108036504,0.000047316244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008590647,0.00010014216,0.000033308577,0.000050142567,0.0000107036285,0.00021343908,0.0011723173,0.000038937727,0.6259802,0.005485047,0.0008776077,0.3660296],"study_design_scores_gemma":[0.00021871888,0.000066180735,0.000016379985,0.00006241143,0.0000049716173,0.00021067195,0.000015899204,0.9240333,0.056063738,0.018844938,0.00019604867,0.00026675107],"about_ca_topic_score_codex":0.000003245525,"about_ca_topic_score_gemma":9.676171e-7,"teacher_disagreement_score":0.92399436,"about_ca_system_score_codex":0.00007321372,"about_ca_system_score_gemma":0.000020013957,"threshold_uncertainty_score":0.52504617},"labels":[],"label_agreement":null},{"id":"W2115634008","doi":"10.1109/crv.2007.62","title":"Super-resolution based on interpolation and global sub pixel translation","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Pixel; Interpolation (computer graphics); Image restoration; Computer vision; Computer science; Artificial intelligence; Image resolution; Point spread function; Iterative reconstruction; Translation (biology); Image sensor; Image (mathematics); Algorithm; Image processing","score_opus":0.017035401842401245,"score_gpt":0.2920206448326118,"score_spread":0.27498524299021054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115634008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003403026,0.000046683857,0.99086463,0.000670034,0.0000525295,0.000095571995,6.117524e-7,0.00051654124,0.0043503665],"genre_scores_gemma":[0.5193508,0.0000011022852,0.48038065,0.00024784796,0.00000968441,0.0000013700129,0.0000014643736,0.0000020631164,0.0000049778014],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993151,0.000017422157,0.0001335595,0.00023142547,0.00015536703,0.00014715023],"domain_scores_gemma":[0.9996367,0.000059954324,0.000033944736,0.00017533224,0.00005066699,0.000043405274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028400085,0.00008195944,0.00005719451,0.000072834766,0.00007146909,0.00007615263,0.0001519115,0.00004628082,0.000002329027],"category_scores_gemma":[0.00003208989,0.000076652694,0.00001684697,0.00024617632,0.000031869135,0.0006759823,0.000024785253,0.000055885084,0.0000038239064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006529169,0.00008345707,0.002208045,0.000018278299,0.0000023299124,0.0000033430872,0.0001773221,0.00006439773,0.06558652,0.03733679,0.00017079579,0.8942834],"study_design_scores_gemma":[0.00019712678,0.000099432844,0.0044352626,0.000022786233,0.000001625877,0.000004757179,0.0000057213992,0.96860754,0.0124530755,0.013715257,0.00034656932,0.00011083764],"about_ca_topic_score_codex":0.000008759474,"about_ca_topic_score_gemma":0.000025205298,"teacher_disagreement_score":0.9685432,"about_ca_system_score_codex":0.00006020328,"about_ca_system_score_gemma":0.00001924068,"threshold_uncertainty_score":0.31258062},"labels":[],"label_agreement":null},{"id":"W2117865218","doi":"10.1109/tip.2011.2108306","title":"Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Deblurring; Sparse approximation; Regularization (linguistics); Pattern recognition (psychology); Image restoration; Image (mathematics); Image processing; Representation (politics); Iterative reconstruction","score_opus":0.02018690805244431,"score_gpt":0.2426133886187176,"score_spread":0.2224264805662733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117865218","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023090024,0.00035662617,0.99561274,0.00011182527,0.00006757552,0.00029971788,0.000007160587,0.0007677296,0.00046760714],"genre_scores_gemma":[0.3983666,0.00004166584,0.6013886,0.00005743567,0.000012381287,0.00005399659,9.1457736e-7,0.000026966221,0.00005146814],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816054,0.00010377869,0.00031249912,0.00077062025,0.0002622663,0.0003902723],"domain_scores_gemma":[0.999133,0.000040708823,0.00017371985,0.00023211051,0.00028579423,0.00013469091],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027534948,0.00031558907,0.00022565931,0.00029387002,0.0007966837,0.00034550583,0.00025193326,0.0001285179,0.0000066174907],"category_scores_gemma":[0.000013881778,0.00033592666,0.00003898233,0.00062342297,0.0002696949,0.0046226433,0.000013447108,0.00037464825,0.0000040740574],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027827063,0.0004135066,0.000023340343,0.0001689756,0.000044794626,0.0000201824,0.007029419,0.00006928476,0.55851835,0.00086043595,0.000098045915,0.43247542],"study_design_scores_gemma":[0.00057867303,0.0003629894,0.000070064154,0.00026664694,0.00004319037,0.00015479165,0.00031376418,0.62483895,0.35506883,0.017717436,0.00003673337,0.0005479527],"about_ca_topic_score_codex":0.000045878758,"about_ca_topic_score_gemma":0.00001499664,"teacher_disagreement_score":0.6247696,"about_ca_system_score_codex":0.00013462965,"about_ca_system_score_gemma":0.000083234205,"threshold_uncertainty_score":0.9999093},"labels":[],"label_agreement":null},{"id":"W2121438396","doi":"10.1109/tip.2009.2028257","title":"Optimized Atom Position and Coefficient Coding for Matching Pursuit-Based Image Compression","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Encoder; Coding (social sciences); Matching pursuit; Context-adaptive binary arithmetic coding; Computer science; Algorithm; Artificial intelligence; JPEG; Image compression; Data compression; Context-adaptive variable-length coding; Computer vision; Mathematics; Image processing; Image (mathematics); Compressed sensing; Statistics","score_opus":0.014041935375023988,"score_gpt":0.2956605979619089,"score_spread":0.28161866258688495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121438396","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005553367,0.0001720897,0.9954923,0.0016845465,0.00013159108,0.0005678141,0.000012498889,0.0012590057,0.00012486542],"genre_scores_gemma":[0.3289956,0.000010602687,0.67024714,0.0006103713,0.000019084588,0.00006793759,0.00000331567,0.000024788336,0.000021149166],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979459,0.000062271,0.00040845983,0.00073270913,0.0003791381,0.0004715171],"domain_scores_gemma":[0.9987702,0.00016144979,0.00023856893,0.00037465504,0.00031329272,0.00014187294],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00038957968,0.00033616804,0.0003179222,0.00034057038,0.0011181682,0.001147341,0.00054372946,0.000102888225,0.0000037827283],"category_scores_gemma":[0.0000137257875,0.00033436876,0.00010569798,0.00045021737,0.00013204724,0.0022783172,0.0000068163927,0.00033112097,0.0000029704431],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016052004,0.00031932705,1.9042618e-7,0.0001976881,0.0000063470634,0.000011809383,0.0004750559,0.0046648434,0.7096055,0.00014442681,0.000048177342,0.2843661],"study_design_scores_gemma":[0.0009424776,0.00015821919,0.000005670111,0.00048706538,0.000023305622,0.000029051325,0.000026511763,0.5539228,0.44064224,0.00343571,0.00003331912,0.00029364706],"about_ca_topic_score_codex":0.0000029763787,"about_ca_topic_score_gemma":2.821e-7,"teacher_disagreement_score":0.54925793,"about_ca_system_score_codex":0.00013488394,"about_ca_system_score_gemma":0.00011844579,"threshold_uncertainty_score":0.99991083},"labels":[],"label_agreement":null},{"id":"W2121608859","doi":"10.1109/crv.2007.65","title":"Training Database Adequacy Analysis for Learning-Based Super-Resolution","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Database; Artificial intelligence; Euclidean distance; Resolution (logic); Function (biology); Construct (python library); Process (computing); Database design; Pattern recognition (psychology); Data mining","score_opus":0.06137323208074984,"score_gpt":0.3440645806534644,"score_spread":0.28269134857271455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121608859","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032826295,0.00006176509,0.99721885,0.00034278812,0.000035427624,0.00014037295,0.0000022308045,0.0011666417,0.00070363784],"genre_scores_gemma":[0.37791404,7.9289794e-7,0.6215972,0.0002542098,0.000022505405,0.000014084009,0.00002307475,0.000007021131,0.00016710036],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986846,0.000027819517,0.00024417072,0.00043664058,0.00021217941,0.00039463126],"domain_scores_gemma":[0.9989341,0.00028846995,0.00009143877,0.00043625705,0.00015888721,0.00009084437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010815884,0.00012722156,0.00017288775,0.00041888864,0.00022056697,0.000115503564,0.0005754467,0.000046802557,0.000014710894],"category_scores_gemma":[0.00037421405,0.000121326666,0.000117233314,0.0012521527,0.000045442048,0.00086591596,0.00009686301,0.0001366335,0.0000045454835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014850774,0.00038242078,0.005306094,0.00014349881,0.00030112307,0.000050223687,0.0024143928,0.027280943,0.06886556,0.05815896,0.0012623073,0.83568597],"study_design_scores_gemma":[0.00022470171,0.000080696234,0.00028479125,0.000011067545,0.000041123556,0.0000016205092,0.000053221866,0.97137845,0.019933619,0.0017737744,0.0060200426,0.00019692483],"about_ca_topic_score_codex":0.000019780306,"about_ca_topic_score_gemma":0.00003921461,"teacher_disagreement_score":0.94409746,"about_ca_system_score_codex":0.00005683985,"about_ca_system_score_gemma":0.00007610038,"threshold_uncertainty_score":0.49475577},"labels":[],"label_agreement":null},{"id":"W2124378283","doi":"10.1109/tip.2008.924279","title":"Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":575,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Interpolation (computer graphics); Bilinear interpolation; Artificial intelligence; Pixel; Nearest-neighbor interpolation; Autoregressive model; Ringing artifacts; Computer vision; Computer science; Image scaling; Multivariate interpolation; Stairstep interpolation; Mathematics; Image resolution; Pattern recognition (psychology); Algorithm; Image processing; Image (mathematics); Statistics","score_opus":0.01898285852528693,"score_gpt":0.28340277366283834,"score_spread":0.2644199151375514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124378283","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015866233,0.00040750232,0.9962395,0.00019996589,0.000119995326,0.00024321036,0.000008801915,0.0010476564,0.00014677756],"genre_scores_gemma":[0.45543483,0.000040352083,0.5443406,0.000074898526,0.000011385349,0.000039057395,0.0000015025608,0.00002463319,0.000032711137],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799633,0.00005251434,0.00043276628,0.0007352622,0.00042986192,0.00035327263],"domain_scores_gemma":[0.9987842,0.00011259419,0.00023013577,0.0003651603,0.00037896534,0.00012897936],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001926665,0.00033034274,0.00026035088,0.0003271549,0.000987057,0.0004081012,0.00043544962,0.00011927249,0.000006334477],"category_scores_gemma":[0.000045331137,0.00032811135,0.00006553542,0.0004378867,0.00023009603,0.0058556385,0.00001365938,0.00043974098,0.00001490649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094778334,0.00017978583,0.0000018535399,0.00006963491,0.000013613704,0.00002711262,0.0026335951,0.004398694,0.08975097,0.000021815107,0.00021066106,0.9025975],"study_design_scores_gemma":[0.0003530715,0.000106989944,0.000003192965,0.00032096228,0.000014122963,0.00014221319,0.00006043165,0.95552886,0.03760136,0.0055306456,0.00000800192,0.00033017472],"about_ca_topic_score_codex":0.000014377521,"about_ca_topic_score_gemma":0.000001518639,"teacher_disagreement_score":0.95113015,"about_ca_system_score_codex":0.00013551807,"about_ca_system_score_gemma":0.00013923895,"threshold_uncertainty_score":0.9999171},"labels":[],"label_agreement":null},{"id":"W2124467867","doi":"10.1109/icip.2005.1529684","title":"Image interpolation using texture orientation map and kernel Fisher discriminant","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial intelligence; Kernel (algebra); Subpixel rendering; Pattern recognition (psychology); Interpolation (computer graphics); Computer vision; Orientation (vector space); Computer science; Kernel Fisher discriminant analysis; Image texture; Mathematics; Context (archaeology); Linear discriminant analysis; Ringing artifacts; Pixel; Image (mathematics); Image processing; Geography","score_opus":0.0152027363183156,"score_gpt":0.30006592198165327,"score_spread":0.2848631856633377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124467867","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032122612,0.00007485389,0.994273,0.0011112129,0.00005813408,0.00008068587,3.7934552e-7,0.0002908877,0.00089857914],"genre_scores_gemma":[0.23420045,0.0000033886313,0.7652521,0.00025861975,0.0000365944,0.0000030511355,0.0000011567087,0.000005986541,0.00023861942],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993716,0.000015138584,0.00012884475,0.000251781,0.000104716804,0.0001279201],"domain_scores_gemma":[0.99963063,0.000014406665,0.00006365049,0.00019436594,0.00006209715,0.000034876306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008161952,0.00008929925,0.00006725631,0.0000601505,0.00008467349,0.00021655491,0.00018714958,0.000031403408,0.00001410169],"category_scores_gemma":[0.000021214611,0.00007413293,0.0000139823505,0.000108708315,0.00004068184,0.0024728507,0.0001639957,0.00006975253,0.0000072073844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014567226,0.0001034524,0.0020124107,0.00008588414,0.000008989468,0.0000098646515,0.004166201,0.000011668262,0.43024427,0.019734534,0.0041346843,0.5394735],"study_design_scores_gemma":[0.00011496524,0.000020593367,0.0007725043,0.000034937526,0.0000042445404,0.000022283535,0.00006167348,0.9716394,0.013807966,0.012028012,0.0013441624,0.00014925412],"about_ca_topic_score_codex":0.000015269372,"about_ca_topic_score_gemma":0.000007870493,"teacher_disagreement_score":0.9716277,"about_ca_system_score_codex":0.00004160412,"about_ca_system_score_gemma":0.000015326354,"threshold_uncertainty_score":0.30230528},"labels":[],"label_agreement":null},{"id":"W2125292059","doi":"10.1109/icassp.1997.595392","title":"An efficient implementation of affine transformation using one-dimensional FFTs","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Affine transformation; Computer science; Image scaling; Transformation (genetics); Interpolation (computer graphics); Algorithm; Robustness (evolution); Fast Fourier transform; Resampling; Image quality; Theoretical computer science; Image (mathematics); Computer vision; Image processing; Mathematics","score_opus":0.039421625542690006,"score_gpt":0.33248597582217193,"score_spread":0.2930643502794819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125292059","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.103195146,0.000021893402,0.8959838,0.00016729433,0.000024417765,0.00010614577,8.071219e-7,0.0002057436,0.0002947653],"genre_scores_gemma":[0.51076084,8.5027335e-7,0.48917925,0.00004550997,0.0000044829476,0.0000018012149,0.0000012471027,0.0000024193675,0.0000035848416],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992598,0.000020828302,0.00021828078,0.00015094817,0.00023021088,0.000119907665],"domain_scores_gemma":[0.99956894,0.000011224228,0.00008521335,0.00019611506,0.0001069485,0.000031583822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012269421,0.00006588303,0.000076140655,0.00010200465,0.0000653758,0.000034464905,0.00022223454,0.000019560635,0.00010152214],"category_scores_gemma":[0.0000033719923,0.0000630297,0.000019448424,0.00023432278,0.000020230156,0.0008882657,0.000028919729,0.000035179943,0.0000033502006],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003966649,0.0003182716,0.00006297015,0.000037259557,0.0000070545066,0.0000015705273,0.0024767846,0.007161371,0.6243727,0.020664237,0.000063312815,0.3448305],"study_design_scores_gemma":[0.00014176349,0.00005748424,0.00015693114,0.000009925122,0.000002291932,0.0000043733894,0.000022455257,0.77445775,0.22420506,0.0008620083,0.000016280783,0.00006370364],"about_ca_topic_score_codex":0.000023854613,"about_ca_topic_score_gemma":0.0000033565677,"teacher_disagreement_score":0.7672964,"about_ca_system_score_codex":0.000036557394,"about_ca_system_score_gemma":0.000014445425,"threshold_uncertainty_score":0.25702766},"labels":[],"label_agreement":null},{"id":"W2125460212","doi":"10.1109/icip.2000.899568","title":"The iterative deconvolution of linearly blurred images using non-parametric stabilizing functions","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Deconvolution; Regularization (linguistics); Iterative method; Parametric statistics; Blind deconvolution; Propagation of uncertainty; Noise (video); Algorithm; Image restoration; Computer science; Mathematics; Mathematical optimization; Image (mathematics); Image processing; Artificial intelligence; Statistics","score_opus":0.033080986402329336,"score_gpt":0.28363043195417786,"score_spread":0.25054944555184855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125460212","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004772243,0.00067568064,0.9924538,0.00019367537,0.00011986889,0.00014424376,0.0000013732094,0.000203508,0.0014356114],"genre_scores_gemma":[0.4405233,0.000026198024,0.5590611,0.000025073845,0.000016258653,0.000008256375,2.2852151e-7,0.000005524437,0.00033408165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990199,0.000046666893,0.0002734358,0.00025756308,0.00018687936,0.00021555249],"domain_scores_gemma":[0.9987438,0.00027997023,0.00016743758,0.00044401115,0.00032766492,0.00003715207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022702644,0.00011082501,0.0001209274,0.00017393961,0.0003590348,0.00022744146,0.00048609066,0.00003437314,0.000012148596],"category_scores_gemma":[0.0002825689,0.000079740086,0.0000513274,0.0011662554,0.00013486842,0.0013388807,0.0001771889,0.00012673847,0.000010611104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026132173,0.00064235204,0.0037903201,0.00011469044,0.000109471366,0.000010934642,0.0036292989,0.0017431858,0.46299225,0.013436354,0.005832883,0.50767213],"study_design_scores_gemma":[0.000109932465,0.00007183521,0.00025420732,0.000025187102,0.0000058966543,0.000009807645,0.000058132257,0.9329949,0.0625458,0.0034946066,0.00030751678,0.00012218516],"about_ca_topic_score_codex":0.000020176958,"about_ca_topic_score_gemma":0.0000025758036,"teacher_disagreement_score":0.9312517,"about_ca_system_score_codex":0.00006990665,"about_ca_system_score_gemma":0.000029599893,"threshold_uncertainty_score":0.3251706},"labels":[],"label_agreement":null},{"id":"W2127306343","doi":"10.1109/iscas.2008.4541405","title":"Multiframe image super-resolution using quasi-newton algorithms","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Algorithm; Computer science; Convergence (economics); Superresolution; Gradient descent; Iterative reconstruction; Resolution (logic); Image (mathematics); Method of steepest descent; Image resolution; Newton's method; Artificial intelligence; Mathematical optimization; Mathematics; Nonlinear system; Artificial neural network","score_opus":0.044297023527977326,"score_gpt":0.3093593320539154,"score_spread":0.2650623085259381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127306343","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003777001,0.000120569406,0.9929574,0.0004957981,0.00012525641,0.00012921501,9.41514e-7,0.0015366207,0.0008571697],"genre_scores_gemma":[0.041302852,0.000023899442,0.95778954,0.00025853497,0.000058114954,0.00000837872,0.0000014861168,0.000016918048,0.00054024876],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985949,0.00004090659,0.00023410861,0.00046538122,0.0002908166,0.00037392924],"domain_scores_gemma":[0.9990583,0.00004123558,0.000071165945,0.0005652381,0.00017275894,0.000091292684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015312132,0.00017390859,0.00016057238,0.00013000699,0.0003104527,0.000108794484,0.0007206335,0.00007717709,0.000014980858],"category_scores_gemma":[0.0000808368,0.00016332457,0.000058597856,0.0004143571,0.0001587581,0.0021371362,0.00027957663,0.00017581503,0.000043078897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016657094,0.0007955238,0.00068029383,0.00006253199,0.000026706904,0.0003988155,0.0025835105,0.00020633012,0.82306767,0.014153567,0.006368555,0.15163985],"study_design_scores_gemma":[0.00016678816,0.000055174336,0.0001417907,0.000018535693,0.0000022698732,0.00023680381,0.000012099885,0.92790407,0.06533252,0.004622195,0.0012548784,0.00025286415],"about_ca_topic_score_codex":0.00014488824,"about_ca_topic_score_gemma":0.0000024758006,"teacher_disagreement_score":0.9276978,"about_ca_system_score_codex":0.00010984453,"about_ca_system_score_gemma":0.00008836872,"threshold_uncertainty_score":0.6660183},"labels":[],"label_agreement":null},{"id":"W2128185708","doi":"10.1109/icassp.2008.4517875","title":"Down-sampling in DCT domain using linear transform with double-sided multiplication for image/video transcoding","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Discrete cosine transform; Upsampling; Computer science; Transcoding; Computer vision; Interpolation (computer graphics); Nyquist–Shannon sampling theorem; Algorithm; Kernel (algebra); Computational complexity theory; Domain (mathematical analysis); Artificial intelligence; Transform coding; Multiplication (music); Mathematics; Image (mathematics); Discrete mathematics","score_opus":0.07380979543442248,"score_gpt":0.33788205097164165,"score_spread":0.26407225553721914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128185708","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025237007,0.000050995135,0.97211194,0.00072917924,0.000024020992,0.00086988264,0.0000015767549,0.0006282204,0.0003471528],"genre_scores_gemma":[0.2400001,0.000015135546,0.7596716,0.00012111236,0.000027199992,0.00011669843,0.0000034484844,0.00002360405,0.000021085572],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836695,0.000017051567,0.0003927999,0.00057884754,0.00021878391,0.0004255489],"domain_scores_gemma":[0.9990932,0.000149962,0.00010500345,0.0004191122,0.00016190531,0.00007087058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034179786,0.00022355515,0.000251877,0.00023309716,0.00028414777,0.00009065831,0.0005725561,0.000074799485,0.0000022029403],"category_scores_gemma":[0.000025854442,0.00019254746,0.000066856235,0.00061719376,0.00009980903,0.0020093257,0.000034878798,0.00017040888,0.0000015375041],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057417224,0.00029892428,0.00050258177,0.0002562439,0.000028626777,0.000046797093,0.0075785224,0.0046726074,0.9295779,0.011289801,0.000038252176,0.04513559],"study_design_scores_gemma":[0.0032791838,0.0001243395,0.00009981601,0.00023278095,0.000008059988,0.00016049195,0.000058492355,0.5663862,0.41755727,0.011197624,0.00037577137,0.0005199269],"about_ca_topic_score_codex":0.0000722282,"about_ca_topic_score_gemma":0.00004776339,"teacher_disagreement_score":0.56171364,"about_ca_system_score_codex":0.00013516242,"about_ca_system_score_gemma":0.00012535935,"threshold_uncertainty_score":0.78518575},"labels":[],"label_agreement":null},{"id":"W2130357180","doi":"10.1109/ccece.2004.1345204","title":"Wireless camera network for image superresolution","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; RANSAC; Process (computing); Wireless network; Projection (relational algebra); Wireless; Image (mathematics); Algorithm","score_opus":0.012887380154470558,"score_gpt":0.27706154646865183,"score_spread":0.2641741663141813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130357180","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003321345,0.000103023485,0.995412,0.001431226,0.00012184719,0.00019250697,7.419366e-7,0.0010636832,0.0013428553],"genre_scores_gemma":[0.05276365,0.000009618526,0.94628286,0.0005922453,0.00009862134,0.000058872327,0.0000021781623,0.000010367921,0.0001815985],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991695,0.000008712024,0.00013058995,0.00028153055,0.00010537298,0.00030427013],"domain_scores_gemma":[0.99944407,0.000031288128,0.000039778402,0.00033128803,0.00010637824,0.000047196663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014334191,0.000097444055,0.00009770935,0.00003228699,0.00015889017,0.00012610387,0.0005396095,0.000037175767,0.0000032205207],"category_scores_gemma":[0.000026794745,0.0000893241,0.000041117142,0.00023097587,0.000049534243,0.0009692758,0.00009183752,0.00006562542,0.000013044042],"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":[0.000015282407,0.00011249686,0.00006001449,0.000049891176,0.000011399443,0.000014218077,0.00034129724,0.0007182863,0.063823186,0.78570145,0.016404273,0.13274823],"study_design_scores_gemma":[0.0006923989,0.00015918855,0.00010963004,0.00006796262,0.0000050958315,0.000036625792,0.000013426587,0.14034744,0.10232231,0.7469661,0.008847906,0.0004319207],"about_ca_topic_score_codex":0.000023626088,"about_ca_topic_score_gemma":0.000009030482,"teacher_disagreement_score":0.13962914,"about_ca_system_score_codex":0.00006905162,"about_ca_system_score_gemma":0.00006640162,"threshold_uncertainty_score":0.36425313},"labels":[],"label_agreement":null},{"id":"W2131152938","doi":"10.1109/icig.2007.129","title":"A New Image Scaling Algorithm Based on the Sampling Theorem of Papoulis and Application to Color Images","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Aliasing; Scaling; Image scaling; Mathematics; Algorithm; Sampling (signal processing); Image (mathematics); Computer science; Artificial intelligence; Classification of discontinuities; Nyquist–Shannon sampling theorem; Curvature; Computer vision; Image processing; Filter (signal processing); Geometry","score_opus":0.0111232273334754,"score_gpt":0.2984833690570348,"score_spread":0.2873601417235594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131152938","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019165248,0.000026103171,0.99542093,0.0022848442,0.000014541128,0.0002852073,0.0000012338764,0.00027758413,0.0014978807],"genre_scores_gemma":[0.055559754,0.0000024881415,0.9432187,0.0011300389,0.0000252716,0.000014204613,5.0414513e-7,0.000008882773,0.00004015678],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991236,0.000019741838,0.00018830034,0.00029512995,0.0001908472,0.00018243583],"domain_scores_gemma":[0.9988467,0.000440361,0.00008876596,0.0004337481,0.00011514736,0.0000752871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007887525,0.0001056759,0.00010685676,0.00009375873,0.00010822952,0.00010746414,0.00051143696,0.00003010402,0.0000049119026],"category_scores_gemma":[0.00012591631,0.00007190749,0.000023206012,0.0003973103,0.00006152852,0.0002569325,0.00016139912,0.00008523776,0.0000037915763],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010208923,0.000020324687,0.000019311654,0.000009045601,0.0000018974758,8.5777776e-7,0.00017128237,0.000046415968,0.11477585,0.017004574,0.000286219,0.867654],"study_design_scores_gemma":[0.00012946938,0.00007871781,0.00034624353,0.00005126689,0.0000035293428,0.0000031192767,0.00005656674,0.3740012,0.5834569,0.041033294,0.00068788254,0.00015181053],"about_ca_topic_score_codex":0.000035797042,"about_ca_topic_score_gemma":0.0000018789898,"teacher_disagreement_score":0.8675022,"about_ca_system_score_codex":0.00002476898,"about_ca_system_score_gemma":0.000037447797,"threshold_uncertainty_score":0.2932302},"labels":[],"label_agreement":null},{"id":"W2133505688","doi":"10.3103/s1060992x11030088","title":"Efficient ghost removal in motion detection with patch-corrected background differentiation","year":2011,"lang":"en","type":"article","venue":"Optical Memory and Neural Networks","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Pixel; Artificial intelligence; Computer science; Computer vision; Frame (networking); Frame rate; Sequence (biology); Noise reduction; Noise (video); Motion detection; Motion (physics); Image (mathematics); Telecommunications","score_opus":0.018926893415077223,"score_gpt":0.2275192710105831,"score_spread":0.2085923775955059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133505688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42253274,0.000038829745,0.5769046,0.000018408984,0.00010838249,0.000095653115,6.8125225e-8,0.00014114805,0.00016015072],"genre_scores_gemma":[0.9156338,0.000007148403,0.08422253,0.000060672526,0.000038568433,0.00001466452,0.0000011939356,0.000009114765,0.000012285218],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990129,0.000065351946,0.00017565378,0.0003698828,0.00012844487,0.0002478048],"domain_scores_gemma":[0.9995474,0.000058529822,0.000067449575,0.00020234557,0.000050220493,0.00007403775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012508458,0.00014069672,0.00013377059,0.00007284005,0.00010274313,0.00007783266,0.00016334349,0.000083870655,0.0000036395973],"category_scores_gemma":[0.000021433803,0.000113287264,0.000018543518,0.00034609955,0.00007975767,0.00031160464,0.0001033694,0.00026722957,8.986823e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043045994,0.00037359618,0.0020340516,0.000040948697,0.000010839949,0.00017820943,0.0005156892,0.0064500943,0.011259844,0.0016783196,0.000005026967,0.97702295],"study_design_scores_gemma":[0.00025914007,0.00018251022,0.016955514,0.000038887476,0.0000058680207,0.00010972609,0.000015819845,0.9744029,0.007204977,0.0006737156,8.2675905e-7,0.00015012703],"about_ca_topic_score_codex":0.000013603284,"about_ca_topic_score_gemma":0.00003286732,"teacher_disagreement_score":0.9768728,"about_ca_system_score_codex":0.000031914515,"about_ca_system_score_gemma":0.00000514406,"threshold_uncertainty_score":0.46197203},"labels":[],"label_agreement":null},{"id":"W2134204812","doi":"10.1109/icassp.2010.5495234","title":"Image interpolation with hidden Markov model","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Interpolation (computer graphics); Hidden Markov model; Image scaling; Artificial intelligence; Computer science; Demosaicing; Stairstep interpolation; Computer vision; Pattern recognition (psychology); Bilinear interpolation; Dependency (UML); Pixel; Nearest-neighbor interpolation; Image (mathematics); Image resolution; Multivariate interpolation; Image processing","score_opus":0.007415174387726852,"score_gpt":0.26188687030941765,"score_spread":0.2544716959216908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134204812","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011214918,0.000002932102,0.97181386,0.00078553543,0.000034232755,0.00006623989,2.419868e-7,0.0008768732,0.025298571],"genre_scores_gemma":[0.19015078,5.2833497e-7,0.8090172,0.00021599408,0.000012977065,0.0000094899415,4.754154e-7,0.0000072658086,0.0005853177],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994178,0.000005702183,0.0000861968,0.00023046412,0.00012497931,0.00013490533],"domain_scores_gemma":[0.999366,0.00001520541,0.00004532499,0.00043092552,0.00010070199,0.000041831277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009004224,0.000082853796,0.00006185243,0.000059269492,0.00005333853,0.00016047718,0.0005621021,0.000030735097,0.000018068718],"category_scores_gemma":[0.000023249504,0.000061504295,0.000013217439,0.00014180412,0.000051718212,0.0015374093,0.00017620373,0.00016473519,0.00001621228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013360171,0.000054160704,0.00027741867,0.000015028938,0.0000051066972,0.000009007228,0.0003728992,0.000002341428,0.6892594,0.038743798,0.0034817758,0.2677657],"study_design_scores_gemma":[0.00006620021,0.000021621512,0.00005428578,0.0000064833152,0.0000010119481,0.0000166044,0.000002951844,0.9469196,0.015631368,0.037090488,0.00008406884,0.000105306324],"about_ca_topic_score_codex":0.000005454198,"about_ca_topic_score_gemma":0.000015848549,"teacher_disagreement_score":0.9469173,"about_ca_system_score_codex":0.000008404507,"about_ca_system_score_gemma":0.000042248677,"threshold_uncertainty_score":0.25080723},"labels":[],"label_agreement":null},{"id":"W2134250348","doi":"10.1109/tmi.2011.2121091","title":"Real-Time Image-Based B-Mode Ultrasound Image Simulation of Needles Using Tensor-Product Interpolation","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Imaging phantom; Interpolation (computer graphics); Computer vision; Orientation (vector space); Computer science; Artificial intelligence; Sagittal plane; Prostate brachytherapy; Similarity (geometry); Brachytherapy; Mathematics; Image (mathematics); Optics; Physics; Geometry","score_opus":0.02464477377622078,"score_gpt":0.3170508635351867,"score_spread":0.2924060897589659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134250348","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004422345,0.000018748879,0.9936576,0.00017589031,0.00022188218,0.00019897401,0.0000069648268,0.0007082583,0.00058932527],"genre_scores_gemma":[0.5168248,0.000007528081,0.48301792,0.0000843999,0.000024727715,0.000007071317,0.0000015276576,0.000021743981,0.0000102947515],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977671,0.0001354187,0.0005453945,0.00054600864,0.0006637448,0.0003423715],"domain_scores_gemma":[0.9982259,0.00036354022,0.00024594308,0.0006396086,0.00035897715,0.00016601285],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047537062,0.00024977725,0.00028085947,0.00041581236,0.00020622366,0.000088341905,0.000662132,0.000072944415,0.00016717748],"category_scores_gemma":[0.00019944677,0.00024740613,0.00012457537,0.0005841989,0.0003982963,0.0018097707,0.000007630393,0.0003762223,0.000019973855],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058167723,0.00032765226,0.000059762002,0.00007678419,0.00002143112,0.000024852367,0.0012458029,0.0056569157,0.9376869,0.00003721918,0.000034646986,0.054769825],"study_design_scores_gemma":[0.00023676535,0.000027305274,0.000042203847,0.00018486794,0.00002254675,0.000022479548,0.000034888275,0.7357992,0.2623515,0.0010935959,0.0000030047433,0.00018162033],"about_ca_topic_score_codex":0.00019840756,"about_ca_topic_score_gemma":0.00000198618,"teacher_disagreement_score":0.7301423,"about_ca_system_score_codex":0.000106580366,"about_ca_system_score_gemma":0.00020054205,"threshold_uncertainty_score":0.9999978},"labels":[],"label_agreement":null},{"id":"W2135194414","doi":"10.1109/isspa.2010.5605447","title":"Single input image super resolution with alias detection","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Alias; Computer science; Interpolation (computer graphics); Sampling (signal processing); Measure (data warehouse); Sample (material); Electronics; Process (computing); Resolution (logic); Image resolution; Image (mathematics); Artificial intelligence; Electronic engineering; Computer vision; Data mining; Engineering; Electrical engineering","score_opus":0.009418140029808463,"score_gpt":0.24117503613966756,"score_spread":0.2317568961098591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135194414","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010033986,0.000008364784,0.9811318,0.0004832545,0.000117296586,0.000085950356,1.8013276e-7,0.0012937916,0.0068454095],"genre_scores_gemma":[0.37394723,6.497891e-7,0.6255846,0.00012536733,0.000029294137,0.000010019065,3.474437e-7,0.000007432605,0.00029508266],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99920547,0.00001594811,0.00010592606,0.00030399146,0.00017134618,0.0001973209],"domain_scores_gemma":[0.9992573,0.000020178342,0.000045765544,0.0004700766,0.00015616092,0.000050510047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001366441,0.000101683356,0.00007162986,0.00008473035,0.00012609904,0.00019558816,0.00039200476,0.000054149597,0.000015009696],"category_scores_gemma":[0.00005960631,0.00007956282,0.000017512628,0.00030112223,0.00008940622,0.0017125648,0.00012860076,0.00020476722,0.00003033821],"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":[0.0000048745396,0.00003292893,0.000043090877,0.000004360443,0.000001387028,0.000004061149,0.00006006639,7.14556e-7,0.9490774,0.0019239928,0.00018958322,0.04865754],"study_design_scores_gemma":[0.00013418414,0.00015835195,0.0002715967,0.0000090504645,0.0000025002676,0.000088632354,0.0000061436745,0.03350863,0.95095634,0.009272237,0.0054082405,0.0001840926],"about_ca_topic_score_codex":0.000030107298,"about_ca_topic_score_gemma":0.00012799134,"teacher_disagreement_score":0.36391324,"about_ca_system_score_codex":0.000029543184,"about_ca_system_score_gemma":0.000028186687,"threshold_uncertainty_score":0.32444778},"labels":[],"label_agreement":null},{"id":"W2139117218","doi":"10.1109/icip.2000.899378","title":"A near exact image expansion scheme for bi-level images","year":2000,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pixel; Computer science; Interpolation (computer graphics); Computer vision; Artificial intelligence; Distortion (music); Image warping; Quantization (signal processing); Image (mathematics); Code (set theory); Image scaling; Image quality; Image processing; Bandwidth (computing)","score_opus":0.02695683975367776,"score_gpt":0.30552619759136757,"score_spread":0.2785693578376898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139117218","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015462871,0.00010318161,0.98969567,0.0011893021,0.000035929468,0.0002607072,0.0000053030058,0.0014250216,0.0057385857],"genre_scores_gemma":[0.0118161775,0.000028264323,0.9824749,0.00067012664,0.000037385547,0.00007668594,0.0000028866682,0.000022025159,0.0048715076],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99873936,0.000018147044,0.00020660837,0.0004881923,0.00018980613,0.00035785633],"domain_scores_gemma":[0.99901676,0.00007195206,0.00005380832,0.0006349264,0.00014060018,0.000081940765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019190117,0.00017451483,0.0001593193,0.00006158469,0.00021518428,0.00039339488,0.0008756025,0.000053947948,0.00017203926],"category_scores_gemma":[0.000074176765,0.00015032793,0.00007060319,0.00028122895,0.00009472521,0.0019451656,0.00015000184,0.000099022654,0.00012044653],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024184012,0.000117261276,0.000022937516,0.000047624297,0.000007485951,0.000013164536,0.00029979993,0.0000042043675,0.20896825,0.0023011994,0.02503083,0.76316303],"study_design_scores_gemma":[0.00082993624,0.00025234543,0.00034614024,0.00009202737,0.0000065515146,0.000051453746,0.000021212949,0.29979545,0.58927804,0.06283262,0.04573609,0.0007581101],"about_ca_topic_score_codex":0.000013459739,"about_ca_topic_score_gemma":7.4722175e-7,"teacher_disagreement_score":0.7624049,"about_ca_system_score_codex":0.000032272208,"about_ca_system_score_gemma":0.00007977272,"threshold_uncertainty_score":0.6130195},"labels":[],"label_agreement":null},{"id":"W2141679432","doi":"10.1109/glocom.1993.318223","title":"Image and video reconstruction using fuzzy logic","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"JPEG; Computer science; Fuzzy logic; Coding (social sciences); Computer vision; Block (permutation group theory); Artificial intelligence; Transform coding; Data compression; Image (mathematics); Discrete cosine transform; Mathematics","score_opus":0.0362646714623098,"score_gpt":0.2799007238267264,"score_spread":0.24363605236441663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141679432","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001729496,0.00024047744,0.98508793,0.00041733705,0.000060250746,0.000046715075,1.15465426e-7,0.0005794802,0.011838215],"genre_scores_gemma":[0.031966537,0.000040703548,0.96756476,0.00025530646,0.000019038793,0.0000020261498,4.5607322e-8,0.000004341517,0.00014725904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944204,0.000014723419,0.000102087455,0.00024018601,0.000070183545,0.00013078604],"domain_scores_gemma":[0.999638,0.000016660419,0.0000471201,0.00021336609,0.000048968297,0.000035860216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007180463,0.00007248735,0.000070827584,0.0000651077,0.00009874356,0.0001547411,0.00019151217,0.000028608514,0.000029327417],"category_scores_gemma":[0.00003727855,0.000064356966,0.000012762796,0.00017557602,0.00007684037,0.0014042321,0.00013713953,0.00006469047,0.000012946864],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.496382e-7,0.000023545714,0.0002455127,0.000021056401,0.0000035004757,0.000014377551,0.00016095785,0.0000019643687,0.13152288,0.018014865,0.0005697901,0.8494206],"study_design_scores_gemma":[0.000110633184,0.000031585605,0.00006067778,0.00002539883,0.000002626372,0.00057267194,0.000013567072,0.7650017,0.0150898285,0.21858443,0.00031514364,0.00019171572],"about_ca_topic_score_codex":0.0000050660624,"about_ca_topic_score_gemma":4.3404808e-7,"teacher_disagreement_score":0.8492289,"about_ca_system_score_codex":0.000022987932,"about_ca_system_score_gemma":0.0000046809946,"threshold_uncertainty_score":0.2624401},"labels":[],"label_agreement":null},{"id":"W2146022893","doi":"10.1109/ism.2008.74","title":"Deblocking of Block-Transform Compressed Images Using Phase-Adaptive Shifted Thresholding","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deblocking filter; Artificial intelligence; Computer science; Quantization (signal processing); Thresholding; Computer vision; Image compression; Transform coding; Peak signal-to-noise ratio; Color Cell Compression; Data compression; Uncompressed video; Pixel; Chrominance; Discrete cosine transform; Algorithm; Pattern recognition (psychology); Image processing; Image (mathematics); Luminance","score_opus":0.058370826834007024,"score_gpt":0.3204563209716003,"score_spread":0.2620854941375933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146022893","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040131565,0.00029418478,0.9558522,0.000097290475,0.00005071505,0.00019817428,0.0000026465932,0.00076843466,0.0026047642],"genre_scores_gemma":[0.5026654,0.000012032617,0.4972277,0.000047831658,0.000012017265,0.0000039714146,4.0061636e-7,0.000011255548,0.000019370884],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99844164,0.000029866738,0.00039192193,0.00041993381,0.00035218394,0.00036443895],"domain_scores_gemma":[0.9989155,0.00009973396,0.00018998698,0.0004741867,0.00024530324,0.000075313976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015813942,0.00021820945,0.00031447853,0.00020754125,0.00028135566,0.0000544282,0.00092601136,0.000063909574,0.000008396569],"category_scores_gemma":[0.000033492437,0.00020102371,0.00008388222,0.00059835904,0.00021728268,0.0013924083,0.00023238936,0.0001790537,0.0000013775049],"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":[0.000051496074,0.00038598504,0.00032215935,0.00006111455,0.000049891267,0.00013625564,0.0016344049,0.00041031637,0.9700075,0.008299684,0.00022438155,0.018416831],"study_design_scores_gemma":[0.0004890756,0.00008678816,0.00004001621,0.00008775275,0.0000071555673,0.000118352786,0.000019664818,0.31231114,0.6785407,0.008038506,0.000047772442,0.00021307776],"about_ca_topic_score_codex":0.000057752175,"about_ca_topic_score_gemma":0.0000014159666,"teacher_disagreement_score":0.46253386,"about_ca_system_score_codex":0.000057330002,"about_ca_system_score_gemma":0.00010431576,"threshold_uncertainty_score":0.8197509},"labels":[],"label_agreement":null},{"id":"W2146640671","doi":"10.1109/icip.2009.5414423","title":"Nonlocal back-projection for adaptive image enlargement","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":131,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Innovation and Technology Fund","keywords":"Artificial intelligence; Computer vision; Computer science; Iterative reconstruction; Ringing artifacts; Image (mathematics); Projection (relational algebra); Process (computing); Iterative method; Image quality; Algorithm","score_opus":0.02103643457817597,"score_gpt":0.30381211741924863,"score_spread":0.2827756828410727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146640671","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009884352,0.000026476442,0.98628324,0.0012130309,0.00007112346,0.00031305992,7.9195513e-7,0.0006378162,0.0114445565],"genre_scores_gemma":[0.012146496,0.0000043344735,0.9858447,0.0010092189,0.000046486497,0.000034088924,0.0000012524198,0.0000053270032,0.0009081244],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991783,0.000012380072,0.00013637665,0.0003233451,0.00012713257,0.00022248206],"domain_scores_gemma":[0.9994542,0.000024853336,0.000053120606,0.00027557617,0.00015255169,0.00003970652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015089681,0.00010452347,0.00009304729,0.00007080996,0.0000949062,0.00010855232,0.00040242553,0.000032887772,0.00001270677],"category_scores_gemma":[0.000028659746,0.000091065005,0.000044005552,0.00019775092,0.000022939948,0.0010180458,0.00007495211,0.000061219704,0.000035891768],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032495234,0.00016875041,0.0000035622488,0.000011794556,0.0000069892567,0.0000041311687,0.00018815677,0.0000042345805,0.03886644,0.10898177,0.018776404,0.83295524],"study_design_scores_gemma":[0.00037027374,0.0007413498,0.00005418121,0.000019656944,0.000003640123,0.000011473957,0.000025354691,0.7149879,0.16256747,0.111166865,0.009790139,0.00026170292],"about_ca_topic_score_codex":0.0000030504061,"about_ca_topic_score_gemma":0.0000010453903,"teacher_disagreement_score":0.8326936,"about_ca_system_score_codex":0.00006332961,"about_ca_system_score_gemma":0.000036505357,"threshold_uncertainty_score":0.37135231},"labels":[],"label_agreement":null},{"id":"W2152161227","doi":"10.1109/ccece.2009.5090241","title":"Improved hybrid demosaicing and color super-resolution implementation using quasi-Newton algorithms","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Demosaicing; Minification; Algorithm; Gradient descent; Resolution (logic); Image (mathematics); Image resolution; Low resolution; Set (abstract data type); Image quality; Function (biology); Artificial intelligence; Superresolution; Method of steepest descent; Computer vision; High resolution; Color image; Mathematics; Image processing; Mathematical optimization; Artificial neural network","score_opus":0.02107238373995241,"score_gpt":0.3279060601446121,"score_spread":0.3068336764046597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152161227","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034329627,0.00011166303,0.9636445,0.0010072438,0.000055157012,0.00022124959,0.0000011990832,0.00056599086,0.00006333833],"genre_scores_gemma":[0.2807545,0.000010929962,0.71870875,0.0004550861,0.000027725982,0.000004589141,0.0000031980514,0.000005881982,0.000029332343],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890155,0.00003331201,0.00023586088,0.00038750985,0.00015008313,0.0002916807],"domain_scores_gemma":[0.9994776,0.000026941481,0.00009478537,0.00023464483,0.00009969876,0.00006629072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002379773,0.0001406287,0.00013145768,0.0001032238,0.00023322171,0.0002136346,0.00025490022,0.000031067753,0.0000039155584],"category_scores_gemma":[0.000025113875,0.00013555886,0.000022916316,0.00021103813,0.000032687207,0.0016066978,0.00011910797,0.00008665946,9.472069e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000062782246,0.000047566984,0.000099339195,0.000009969792,0.000004353679,0.0000060711213,0.0002450912,0.000016460028,0.46817037,0.0039140624,0.00021148779,0.52726895],"study_design_scores_gemma":[0.00024904055,0.00021461476,0.0002236935,0.00001369792,0.000005406275,0.00005131366,0.000049039638,0.8424395,0.1478197,0.008623012,0.00014526714,0.00016573774],"about_ca_topic_score_codex":0.00014137937,"about_ca_topic_score_gemma":0.000011214891,"teacher_disagreement_score":0.842423,"about_ca_system_score_codex":0.000114574934,"about_ca_system_score_gemma":0.000054314,"threshold_uncertainty_score":0.552793},"labels":[],"label_agreement":null},{"id":"W2152633874","doi":"10.1109/icme.2007.4284936","title":"Edge-Guided Perceptual Image Coding via Adaptive Interpolation","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Computer vision; Image compression; Human visual system model; Artificial intelligence; Encoder; JPEG; Coding (social sciences); JPEG 2000; Image quality; Data compression; Enhanced Data Rates for GSM Evolution; Interpolation (computer graphics); Image processing; Image (mathematics); Mathematics","score_opus":0.029744171498154216,"score_gpt":0.3167359518203906,"score_spread":0.2869917803222364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152633874","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039932394,0.000019715106,0.9691707,0.00016922262,0.00011814035,0.00010745461,2.2637248e-7,0.0011329929,0.028882254],"genre_scores_gemma":[0.36606398,0.0000013234452,0.63332605,0.00026216515,0.00003951447,0.0000027610806,6.5465235e-7,0.000007442627,0.00029610406],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989235,0.000021367645,0.00024169727,0.00033509795,0.00019420208,0.00028416014],"domain_scores_gemma":[0.9992374,0.00008190831,0.00008816907,0.0003280357,0.00019232986,0.00007211862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043056745,0.0001292794,0.00010997086,0.00014770142,0.00011802964,0.00012400496,0.0005856666,0.00004974359,0.000049383216],"category_scores_gemma":[0.00007974583,0.0001201655,0.00003901476,0.00031148308,0.00008238893,0.001712638,0.00030490925,0.00014408474,0.00006780444],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020907431,0.00007221552,0.0001787538,0.000012999328,0.000010471988,0.00003607151,0.0029419004,0.0000017361839,0.65950197,0.06727228,0.005410185,0.2645405],"study_design_scores_gemma":[0.00031655317,0.00017532629,0.001253284,0.000062961364,0.00000549574,0.0001027106,0.00035108387,0.7211385,0.22256124,0.05267118,0.00082790304,0.0005337877],"about_ca_topic_score_codex":0.000016445207,"about_ca_topic_score_gemma":0.0000067621754,"teacher_disagreement_score":0.72113675,"about_ca_system_score_codex":0.00009139235,"about_ca_system_score_gemma":0.000027815719,"threshold_uncertainty_score":0.4900207},"labels":[],"label_agreement":null},{"id":"W2153350057","doi":"","title":"Adaptive large scale artifact reduction in edge-based image super-resolution","year":2007,"lang":"en","type":"article","venue":"IEEE International Conference on Signal and Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Artifact (error); Enhanced Data Rates for GSM Evolution; Resolution (logic); Sub-pixel resolution; Scale (ratio); Image resolution; Low resolution; Frame (networking); Image (mathematics); Reduction (mathematics); Constraint (computer-aided design); High resolution; Pattern recognition (psychology); Image processing; Mathematics; Digital image processing; Remote sensing","score_opus":0.04041796043719761,"score_gpt":0.3263335401509604,"score_spread":0.2859155797137628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153350057","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0134232,0.00007829421,0.98078007,0.0009908718,0.0001872449,0.00015619987,0.000005935743,0.0002516086,0.0041265786],"genre_scores_gemma":[0.6990589,0.000008687544,0.3005565,0.00018132491,0.00009017969,0.000017465793,0.000005979928,0.00001185896,0.00006910982],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998021,0.00005076315,0.00040499796,0.0006292116,0.00047442774,0.00041960517],"domain_scores_gemma":[0.99886006,0.00005103462,0.00019932895,0.00017374656,0.0006115864,0.00010423742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006878014,0.00024198365,0.0001976311,0.0004384377,0.0001991037,0.00053053553,0.00054125057,0.00008839198,0.000039863207],"category_scores_gemma":[0.00005265194,0.00023650669,0.000045025703,0.00036300637,0.0001927187,0.002721437,0.000095605894,0.0003742046,0.00002074708],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032597725,0.0004132322,0.00025464725,0.00005494055,0.000009177204,0.00007004123,0.0010133594,0.000041529824,0.8177002,0.003970088,0.00012264037,0.17602415],"study_design_scores_gemma":[0.0006603562,0.00016730097,0.0010496483,0.00046278906,0.000004523285,0.000040006693,0.00028127953,0.6790052,0.29513457,0.022716314,0.00010909319,0.0003688793],"about_ca_topic_score_codex":0.000020791986,"about_ca_topic_score_gemma":0.000018261417,"teacher_disagreement_score":0.6856357,"about_ca_system_score_codex":0.00017943513,"about_ca_system_score_gemma":0.0001907873,"threshold_uncertainty_score":0.9644463},"labels":[],"label_agreement":null},{"id":"W2154798029","doi":"10.1109/icpr.2008.4761110","title":"Image inpainting using wavelet-based inter- and intra-scale dependency","year":2008,"lang":"en","type":"article","venue":"Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Inpainting; Artificial intelligence; Computer vision; Wavelet; Wavelet transform; Computer science; Image (mathematics); Pattern recognition (psychology); Image restoration; Image texture; Texture synthesis; Pyramid (geometry); Dependency (UML); Scale (ratio); Image processing; Mathematics","score_opus":0.08755463669486882,"score_gpt":0.3095570484795431,"score_spread":0.22200241178467425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154798029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4054487,0.000017659406,0.52959996,0.0061779222,0.0017361899,0.0012572173,0.00048628173,0.001623953,0.053652126],"genre_scores_gemma":[0.892955,0.0003509211,0.10174073,0.003138669,0.00071548345,0.00041467603,0.0003949928,0.0001476551,0.00014187532],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.992225,0.00005272485,0.0016718043,0.002690382,0.0022019378,0.0011581525],"domain_scores_gemma":[0.99086785,0.00015837273,0.0016553549,0.00032365718,0.006478456,0.0005163083],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00087327656,0.0013056257,0.00085559464,0.0018475426,0.0007937494,0.0024421473,0.0026824446,0.0005029427,0.0013176133],"category_scores_gemma":[0.00056644913,0.0014287325,0.00032581564,0.00066562596,0.0005416943,0.005365864,0.0007791041,0.0016282995,0.00045491676],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006407453,0.0019115817,0.020047914,0.00056830497,0.00050927926,0.00018845232,0.0027895637,0.0000016225273,0.095708825,0.0065977145,0.0012514575,0.86978453],"study_design_scores_gemma":[0.005982319,0.001657191,0.005523316,0.007148479,0.00016843685,0.001711484,0.0020773166,0.78441495,0.1146401,0.071606144,0.00035364172,0.0047166217],"about_ca_topic_score_codex":0.00013436672,"about_ca_topic_score_gemma":0.000025893576,"teacher_disagreement_score":0.8650679,"about_ca_system_score_codex":0.0007521847,"about_ca_system_score_gemma":0.00026661818,"threshold_uncertainty_score":0.99996954},"labels":[],"label_agreement":null},{"id":"W2156341121","doi":"10.1109/icip.1995.529754","title":"Image interpolation using a simple Gibbs random field model","year":2002,"lang":"en","type":"article","venue":"Proceedings - International Conference on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Interpolation (computer graphics); Image scaling; Computer science; Nearest-neighbor interpolation; Image (mathematics); Algorithm; Artificial intelligence; Iterative method; Random field; Linear interpolation; Computer vision; Image texture; Binary image; Mathematics; Image processing; Pattern recognition (psychology); Statistics","score_opus":0.06307020455252375,"score_gpt":0.33699807413357014,"score_spread":0.27392786958104637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156341121","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024113946,0.000080215555,0.9456464,0.0023493462,0.00014060283,0.00027321637,0.0000054196,0.0009189868,0.04817439],"genre_scores_gemma":[0.4832417,0.000029309955,0.5156716,0.00067931303,0.00009776694,0.000044574906,0.0000033595109,0.000030870448,0.00020150626],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970319,0.000016896116,0.0006319916,0.0009621271,0.000806773,0.0005503053],"domain_scores_gemma":[0.99729985,0.00006182045,0.00056400296,0.00026652822,0.0016524452,0.00015537163],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003554913,0.00044490595,0.00035010523,0.00050725235,0.00040678855,0.0023731978,0.0018611619,0.00014229407,0.00021213324],"category_scores_gemma":[0.00065223273,0.0004483165,0.00011785743,0.00051701156,0.0001477496,0.007169249,0.00051923515,0.00055613654,0.000050170453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021637556,0.00045112355,0.00026792428,0.00031443097,0.00006161224,0.000030745465,0.0046468866,0.000059064965,0.8125615,0.04850239,0.003470522,0.1294174],"study_design_scores_gemma":[0.0006652616,0.00007006128,0.000004519671,0.00034453266,0.000012427588,0.000053569212,0.00012496981,0.9084492,0.022073148,0.0676419,0.000106962994,0.00045350246],"about_ca_topic_score_codex":0.000008130933,"about_ca_topic_score_gemma":6.4649e-7,"teacher_disagreement_score":0.9083901,"about_ca_system_score_codex":0.00020562607,"about_ca_system_score_gemma":0.000114839626,"threshold_uncertainty_score":0.99979687},"labels":[],"label_agreement":null},{"id":"W2157190232","doi":"10.1109/tip.2006.877407","title":"An edge-guided image interpolation algorithm via directional filtering and data fusion","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1040,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Interpolation (computer graphics); Stairstep interpolation; Algorithm; Ringing artifacts; Pixel; Mathematics; Bilinear interpolation; Nearest-neighbor interpolation; Image scaling; Linear interpolation; Artificial intelligence; Image resolution; Computer science; Spline interpolation; Computer vision; Image processing; Image (mathematics); Pattern recognition (psychology)","score_opus":0.02228654803323153,"score_gpt":0.3156827446838025,"score_spread":0.29339619665057093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157190232","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041700734,0.00015280399,0.9971861,0.00022589062,0.00024149398,0.00018869668,0.000031011998,0.0012942881,0.00026269053],"genre_scores_gemma":[0.18077691,0.000016772881,0.81884784,0.00008700157,0.00010732812,0.000031962736,0.000026048534,0.0000364109,0.000069736416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784404,0.00006969902,0.00041599703,0.00097342837,0.0003481689,0.0003486641],"domain_scores_gemma":[0.9985553,0.000053125874,0.00018740643,0.0008486139,0.00025386806,0.00010169713],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000345632,0.0002952256,0.00020955985,0.00033279016,0.00075731706,0.0009614898,0.0009138552,0.000091932125,0.000015799946],"category_scores_gemma":[0.00001062484,0.00030976627,0.000037831054,0.00053737627,0.00017314086,0.00838815,0.00003362954,0.0003468473,0.000008843037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000075103467,0.00013136302,0.0000025885595,0.000042557986,0.0000035239714,0.000008232578,0.00009887322,0.00005305759,0.41823798,0.000004642434,0.000091065514,0.5813186],"study_design_scores_gemma":[0.00023997958,0.000056116718,0.00005564485,0.0001258111,0.000015075806,0.00015252267,0.000015426138,0.8248481,0.17102864,0.0030354818,0.0001196112,0.00030758575],"about_ca_topic_score_codex":0.000066786655,"about_ca_topic_score_gemma":0.000012598958,"teacher_disagreement_score":0.82479507,"about_ca_system_score_codex":0.000086983826,"about_ca_system_score_gemma":0.00009678448,"threshold_uncertainty_score":0.99993545},"labels":[],"label_agreement":null},{"id":"W2158013100","doi":"10.1109/icassp.2003.1199117","title":"Crafting the observation model for regularized image up-sampling","year":2003,"lang":"en","type":"article","venue":"2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Fidelity; Interpolation (computer graphics); Computer science; Sampling (signal processing); Image (mathematics); Term (time); Image scaling; Artificial intelligence; Computer vision; Image processing; Algorithm","score_opus":0.09244887631372706,"score_gpt":0.33290017123925114,"score_spread":0.24045129492552408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158013100","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015853762,0.0001047054,0.9886878,0.0006305317,0.0005896743,0.00081889774,0.000026732705,0.00051418523,0.0070421095],"genre_scores_gemma":[0.19548935,0.00011311404,0.79948753,0.0012196725,0.00023711497,0.00018710527,0.000016224,0.000064423766,0.0031854955],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99611175,0.00003619036,0.000868485,0.0012084977,0.0010029917,0.000772056],"domain_scores_gemma":[0.9901719,0.00005767533,0.0008126206,0.00030763683,0.008443783,0.00020634917],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012502711,0.0006080645,0.00047048085,0.00027668712,0.00091991643,0.002126966,0.0015669438,0.00027231534,0.00003323747],"category_scores_gemma":[0.0023076518,0.0005138875,0.00008101705,0.0008226462,0.0003610441,0.002287607,0.00015519939,0.0007079263,0.00001580232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024108401,0.00037651215,0.00024348596,0.00047713763,0.0001090121,0.000013615144,0.002071225,0.0006493933,0.24839541,0.7068066,0.010232148,0.030384397],"study_design_scores_gemma":[0.0007787497,0.00012995793,0.000027023518,0.00031710358,0.000038908038,0.00006009212,0.00025997407,0.93530345,0.016497975,0.04441739,0.001558919,0.00061047205],"about_ca_topic_score_codex":0.00000593239,"about_ca_topic_score_gemma":0.0000018102096,"teacher_disagreement_score":0.93465406,"about_ca_system_score_codex":0.00022830427,"about_ca_system_score_gemma":0.00083286647,"threshold_uncertainty_score":0.99973124},"labels":[],"label_agreement":null},{"id":"W2162827400","doi":"10.5539/cis.v7n2p68","title":"Robust Spatial-Domain Based Super-Resolution Mosaicing of CubeSat Video Frames: Algorithm and Evaluation","year":2014,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Algorithm; CubeSat; Artificial intelligence; Computer vision; Image resolution; Singular value decomposition; Noise (video); Image (mathematics); Satellite","score_opus":0.016838916524570265,"score_gpt":0.26256634880275304,"score_spread":0.24572743227818278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162827400","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005719405,0.0000363535,0.9932738,0.00025378595,0.00013226779,0.00018749437,0.0000012936287,0.00012043448,0.0002751722],"genre_scores_gemma":[0.28735235,0.000007263591,0.7121945,0.00041425595,0.00001893978,0.000007378212,0.0000030967242,0.0000017178901,4.8047116e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985635,0.000056322482,0.00033572543,0.00024290061,0.00061244733,0.00018906401],"domain_scores_gemma":[0.9987412,0.000086741,0.00019038827,0.0003099278,0.0005907632,0.00008097179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020609389,0.000110073364,0.00013179192,0.00034048982,0.00027960358,0.00038835092,0.000447118,0.00003894618,0.0000015313274],"category_scores_gemma":[0.00015414268,0.000099586294,0.000015381034,0.000608626,0.00034255808,0.010032665,0.0002863245,0.00007971663,0.0000019651554],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015130046,0.0000079669335,0.0001110337,0.000029932093,7.5090605e-7,4.4454367e-8,0.0009743222,0.0030096434,0.00070427184,0.0033845108,0.00004414567,0.9917319],"study_design_scores_gemma":[0.00029250205,0.000098534925,0.0034902461,0.000067555105,0.000002441913,0.000007505751,0.000019078552,0.98753303,0.0037126588,0.0040164576,0.00064216723,0.00011784467],"about_ca_topic_score_codex":0.000021499298,"about_ca_topic_score_gemma":5.6415075e-7,"teacher_disagreement_score":0.99161404,"about_ca_system_score_codex":0.00005015677,"about_ca_system_score_gemma":0.00012955298,"threshold_uncertainty_score":0.72734404},"labels":[],"label_agreement":null},{"id":"W2163456057","doi":"10.1109/icip.2007.4379093","title":"Fast Super-Resolution for Rational Magnification Factors","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Magnification; Circulant matrix; Resolution (logic); Pixel; Computer science; Image resolution; Set (abstract data type); Image (mathematics); Algorithm; Artificial intelligence","score_opus":0.027925084481087083,"score_gpt":0.3088023329934736,"score_spread":0.2808772485123865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163456057","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00071634905,0.000023019216,0.99582523,0.00066722004,0.000085176725,0.00017241406,0.0000013844336,0.00055480015,0.0019544335],"genre_scores_gemma":[0.2113016,0.0000011246241,0.78784424,0.00011151687,0.000033083146,0.000013890847,0.000012720617,0.000004416949,0.00067742233],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993305,0.000006027321,0.00014616549,0.00021466309,0.00013560642,0.00016703401],"domain_scores_gemma":[0.9994752,0.000072155264,0.00004568241,0.00019993128,0.00016992392,0.000037140744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002574312,0.00006924265,0.0000497977,0.00007986525,0.00012156473,0.00007680107,0.00030686345,0.000035812336,0.000011841341],"category_scores_gemma":[0.00008417253,0.00006157437,0.00002687504,0.00016170721,0.000026308036,0.00087833864,0.00004458291,0.000039078335,0.0000069422954],"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":[0.000015701093,0.00011482026,0.0012775741,0.00003199661,0.000005045256,7.8118757e-7,0.00056874304,0.000038068432,0.09966399,0.709809,0.0040646074,0.18440966],"study_design_scores_gemma":[0.00048973685,0.00019741715,0.024525551,0.000018303228,0.000006489559,0.000011108326,0.00014559348,0.36517268,0.37951908,0.20129657,0.028045926,0.0005715288],"about_ca_topic_score_codex":0.000004072711,"about_ca_topic_score_gemma":0.0000075575995,"teacher_disagreement_score":0.50851244,"about_ca_system_score_codex":0.00005681949,"about_ca_system_score_gemma":0.000033789063,"threshold_uncertainty_score":0.251093},"labels":[],"label_agreement":null},{"id":"W2163970352","doi":"10.1109/tip.2006.873446","title":"A segmentation-based regularization term for image deconvolution","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Maximum a posteriori estimation; Blind deconvolution; Deconvolution; Image restoration; Mathematics; Artificial intelligence; Image segmentation; Regularization (linguistics); Segmentation; Pattern recognition (psychology); Prior probability; Algorithm; Bayesian probability; Image processing; Computer science; Image (mathematics); Statistics; Maximum likelihood","score_opus":0.010755402395461789,"score_gpt":0.2777735855766936,"score_spread":0.2670181831812318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163970352","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002118643,0.00007525776,0.99689096,0.0005544391,0.00016072993,0.0005499348,0.000014608897,0.001281613,0.00026059488],"genre_scores_gemma":[0.2239672,0.0000022472443,0.7751336,0.00019698379,0.000046005014,0.00034148977,0.000015482447,0.00003689682,0.00026008324],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998288,0.00004149338,0.00040553825,0.0006048766,0.000289394,0.0003706929],"domain_scores_gemma":[0.99877226,0.00008968277,0.00024639518,0.00038102953,0.00045135315,0.000059261605],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021972637,0.0002586009,0.00018376122,0.00034186165,0.0007187535,0.0006411636,0.00043778954,0.000093069924,0.000008132276],"category_scores_gemma":[0.000015440022,0.00027996325,0.00010782989,0.00063694443,0.00012880679,0.0028932057,0.0000026423468,0.00016737402,0.000010879269],"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":[0.000051436225,0.00038742746,0.000008565221,0.0003185245,0.000008343975,0.000005928173,0.00014855298,0.0024899605,0.7216957,0.00028140648,0.00022881314,0.2743753],"study_design_scores_gemma":[0.0005496316,0.00006235126,0.000022943083,0.00010199089,0.000019542886,0.000009272333,0.000009481403,0.43750566,0.5519524,0.009473664,0.00004764109,0.0002453957],"about_ca_topic_score_codex":0.000008728273,"about_ca_topic_score_gemma":0.0000063639386,"teacher_disagreement_score":0.4350157,"about_ca_system_score_codex":0.00021894745,"about_ca_system_score_gemma":0.00022684115,"threshold_uncertainty_score":0.99996525},"labels":[],"label_agreement":null},{"id":"W2166011696","doi":"10.1109/crv.2007.40","title":"Images Restoration Using an Iterative Dynamic Programming Approach","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Grayscale; Streak; Image restoration; Computer science; Dynamic programming; Impulse noise; Artificial intelligence; Noise (video); Computer vision; Minification; Gaussian noise; Iterative method; Impulse (physics); Algorithm; Mathematical optimization; Image (mathematics); Pixel; Image processing; Mathematics","score_opus":0.03086780617327794,"score_gpt":0.3443093920190218,"score_spread":0.31344158584574383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166011696","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026522493,0.000059243095,0.99426585,0.000038993563,0.00004123059,0.00018578109,1.9280371e-7,0.0010720453,0.0016844135],"genre_scores_gemma":[0.25932062,6.9279514e-7,0.7404958,0.00006367566,0.000018670014,0.000005917205,0.0000031287937,0.000008745324,0.000082787206],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989362,0.00003628872,0.00019007198,0.00038140191,0.00018912708,0.00026691795],"domain_scores_gemma":[0.99927807,0.000022473781,0.00009686362,0.00037843877,0.00015897305,0.000065153334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005188003,0.00012235268,0.000094175004,0.00014535995,0.0001873393,0.00038137392,0.00045855853,0.000045368328,9.14254e-7],"category_scores_gemma":[0.000036494246,0.000110626366,0.000020238784,0.0004433672,0.00006127354,0.0033263715,0.00012829433,0.000116502786,0.000001628631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011338665,0.0003336037,0.00028882694,0.000038970713,0.000007876055,0.000027233238,0.0025325206,0.00020915784,0.18502954,0.017874388,0.000044851196,0.7936017],"study_design_scores_gemma":[0.00010067811,0.0001041003,0.00023854207,0.000019645877,0.0000030272224,0.000050994036,0.00016679562,0.9431717,0.047076996,0.008517962,0.00027415602,0.00027539962],"about_ca_topic_score_codex":0.0000121723915,"about_ca_topic_score_gemma":0.0000049726077,"teacher_disagreement_score":0.9429625,"about_ca_system_score_codex":0.000108919055,"about_ca_system_score_gemma":0.000041536605,"threshold_uncertainty_score":0.45112124},"labels":[],"label_agreement":null},{"id":"W2166926403","doi":"10.1109/icip.2007.4379641","title":"Sound from Gramophone Record Groove Surface Orientation","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Orientation (vector space); Groove (engineering); Acoustics; Sound (geography); Computer science; Focus (optics); Computer vision; SIGNAL (programming language); Optics; Artificial intelligence; Materials science; Physics; Mathematics; Geometry","score_opus":0.015924577987954623,"score_gpt":0.2947270725370179,"score_spread":0.2788024945490633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166926403","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046800654,0.00006534713,0.94844776,0.00015658542,0.00030928556,0.00008004369,7.4180025e-7,0.0009742442,0.0031653517],"genre_scores_gemma":[0.26112282,0.000005519792,0.7381358,0.00030417807,0.00003605713,0.0000013362563,0.0000030601568,0.0000071963445,0.00038399777],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99898934,0.0000152511775,0.00019992825,0.0003594442,0.00020429623,0.00023175517],"domain_scores_gemma":[0.9991914,0.00012930397,0.000090227106,0.00041611935,0.000106554355,0.00006639803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002653755,0.000104634026,0.000095236894,0.000045951667,0.000086739914,0.00014374842,0.0005073837,0.000047009537,0.000028970213],"category_scores_gemma":[0.0000529876,0.00009862045,0.000025438698,0.00040452817,0.000034820943,0.0010886289,0.00015530846,0.000100549696,0.000082000544],"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":[0.000061934064,0.00024825186,0.015570457,0.000019815818,0.000028497167,0.00009573021,0.0025319173,0.000029884668,0.17319943,0.06385799,0.005929464,0.7384266],"study_design_scores_gemma":[0.00028466177,0.00007747795,0.0036234455,0.00001591616,0.000003647267,0.0000071432933,0.00008468924,0.015688721,0.20088317,0.77622306,0.0027625996,0.000345472],"about_ca_topic_score_codex":0.00020985575,"about_ca_topic_score_gemma":0.000081764665,"teacher_disagreement_score":0.73808116,"about_ca_system_score_codex":0.0000625723,"about_ca_system_score_gemma":0.000021430818,"threshold_uncertainty_score":0.4021625},"labels":[],"label_agreement":null},{"id":"W2166982189","doi":"10.1109/pacrim.1999.799486","title":"Image expansion using segmentation-based method","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Distortion (music); Interpolation (computer graphics); Pixel; Image segmentation; Spline (mechanical); Segmentation; Image processing; Image (mathematics)","score_opus":0.029096035230034047,"score_gpt":0.36005037559264685,"score_spread":0.3309543403626128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166982189","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012869411,0.00004148971,0.99594796,0.000115084535,0.000054867156,0.0000898946,1.6894846e-7,0.0005935549,0.0030283039],"genre_scores_gemma":[0.0059478516,0.0000010572599,0.99330497,0.00064847915,0.0000053306767,0.000008437894,4.609458e-7,0.000008767321,0.00007463226],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991882,0.00009110026,0.00013308154,0.00026816144,0.00015913213,0.00016035356],"domain_scores_gemma":[0.9993868,0.00007518371,0.00006084535,0.0003369106,0.00009757342,0.000042694304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028699765,0.00009203484,0.00008055615,0.00008359799,0.000106939224,0.000127335,0.00028472178,0.00002542988,0.000033255466],"category_scores_gemma":[0.00008843063,0.00008299333,0.000027300757,0.00032633977,0.000021411026,0.00087197754,0.000043206437,0.000057926303,0.0000099122235],"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":[0.0000010543712,0.000036931688,0.000045189096,0.000013082501,0.0000018904622,0.0000069580324,0.000083330786,0.00015856548,0.951039,0.010046636,0.00016461391,0.038402755],"study_design_scores_gemma":[0.00009015569,0.000010674931,0.000003502896,0.000007936917,0.0000014370725,0.000006340016,0.000010018216,0.43909773,0.5466498,0.013847403,0.0001875152,0.000087466],"about_ca_topic_score_codex":0.000007825228,"about_ca_topic_score_gemma":2.9292391e-7,"teacher_disagreement_score":0.43893915,"about_ca_system_score_codex":0.000045607572,"about_ca_system_score_gemma":0.00010288288,"threshold_uncertainty_score":0.33843696},"labels":[],"label_agreement":null},{"id":"W2168561313","doi":"10.1109/tip.2005.846019","title":"Specification of the observation model for regularized image up-sampling","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Regularization (linguistics); Image resolution; Inverse problem; Computer vision; Artificial intelligence; Computer science; Fidelity; Image (mathematics); Sampling (signal processing); Mathematics; Algorithm","score_opus":0.060697355152161334,"score_gpt":0.3180474375298847,"score_spread":0.25735008237772333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168561313","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006181194,0.000053708467,0.9967418,0.0014193895,0.0001451606,0.0004785673,0.0000083631185,0.0004359077,0.00009899104],"genre_scores_gemma":[0.27050075,0.000007747782,0.72879785,0.0001708693,0.000034550892,0.0000829338,0.0000010985038,0.000025745985,0.0003784264],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838275,0.000028253162,0.0004991304,0.00045459435,0.00034392904,0.00029134165],"domain_scores_gemma":[0.99821466,0.000101192505,0.0003771478,0.0006330396,0.0006273417,0.000046594887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004742073,0.00020054015,0.00019158385,0.00017577255,0.0005243982,0.00022778915,0.00084340625,0.00008833268,0.0000022403806],"category_scores_gemma":[0.00004832366,0.00017327136,0.00013591956,0.00066781254,0.00015137093,0.0026837885,0.0000073703427,0.00024740273,0.0000026101686],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003789934,0.00010295723,7.5579686e-7,0.000114772745,0.0000055920186,7.787119e-8,0.00078102027,0.006370075,0.6496673,0.00033215378,0.00003261473,0.34255475],"study_design_scores_gemma":[0.00022477662,0.000011197333,0.000009358255,0.000088187706,0.000011958345,0.0000027304752,0.000015790672,0.5688008,0.42536846,0.0052910154,0.00006436526,0.00011137375],"about_ca_topic_score_codex":0.0000018312944,"about_ca_topic_score_gemma":0.000004103764,"teacher_disagreement_score":0.56243074,"about_ca_system_score_codex":0.00013908584,"about_ca_system_score_gemma":0.00017626812,"threshold_uncertainty_score":0.7065801},"labels":[],"label_agreement":null},{"id":"W2170965888","doi":"10.1109/tip.2005.851684","title":"Image up-sampling using total-variation regularization with a new observation model","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":352,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Total variation denoising; Fidelity; Regularization (linguistics); Ringing artifacts; Ringing; Image processing; Mathematics; Artificial intelligence; Computer science; Human visual system model; Motion estimation; Algorithm; Sampling (signal processing); Computer vision; Image (mathematics); Enhanced Data Rates for GSM Evolution","score_opus":0.041368283786374985,"score_gpt":0.2987543564115658,"score_spread":0.2573860726251908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170965888","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013775214,0.00007529562,0.99582565,0.000848002,0.00010191998,0.0003261007,0.0000031843185,0.0013288386,0.00011346804],"genre_scores_gemma":[0.15799548,0.0000083947425,0.841057,0.00025287585,0.000082374834,0.000032963027,0.0000035231994,0.00005976093,0.00050764537],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775964,0.000037362664,0.00047024616,0.000764513,0.00052692334,0.0004413019],"domain_scores_gemma":[0.9984042,0.00004102088,0.0003534629,0.00054605625,0.0005162525,0.00013902715],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024835108,0.00035569776,0.00024855314,0.0003651392,0.00074224605,0.0009613686,0.00047171762,0.00012851502,0.000007489378],"category_scores_gemma":[0.0000244729,0.000359603,0.00008403665,0.0011788375,0.0000828117,0.009336212,0.000008093901,0.0003787725,0.000010322376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057018977,0.00013564448,0.0000019740269,0.00009623814,0.000017400456,0.0000020439893,0.0018760492,0.3115555,0.42200324,0.00013838384,0.00001569025,0.2641008],"study_design_scores_gemma":[0.0004766394,0.000044979522,0.000013310689,0.00027996246,0.000042999567,0.00005054004,0.00002166699,0.8768388,0.118036166,0.003799204,0.000013950389,0.0003817884],"about_ca_topic_score_codex":0.000021795977,"about_ca_topic_score_gemma":0.000007705697,"teacher_disagreement_score":0.5652833,"about_ca_system_score_codex":0.00036384803,"about_ca_system_score_gemma":0.0005747968,"threshold_uncertainty_score":0.9998856},"labels":[],"label_agreement":null},{"id":"W2172145497","doi":"10.1109/icip.2002.1038990","title":"Isophote estimation by cubic-spline interpolation","year":2003,"lang":"en","type":"article","venue":"Proceedings - International Conference on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Monotone cubic interpolation; Spline interpolation; Interpolation (computer graphics); Bicubic interpolation; Pixel; Spline (mechanical); Cubic Hermite spline; Mathematics; Multivariate interpolation; Nearest-neighbor interpolation; Thin plate spline; Smoothing spline; Image scaling; Computer vision; Artificial intelligence; Computer science; Image (mathematics); Physics; Image processing; Bilinear interpolation","score_opus":0.024369676031744072,"score_gpt":0.31781733780409227,"score_spread":0.2934476617723482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172145497","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011440258,0.00012992421,0.93496823,0.002173361,0.00024012559,0.00022685573,0.000005582952,0.0008903001,0.06022158],"genre_scores_gemma":[0.50095993,0.000029705796,0.49802843,0.00041579737,0.000038035476,0.00006321079,0.000013382213,0.000026762169,0.00042472914],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973458,0.000021020865,0.0005600412,0.0008861591,0.00075368554,0.00043328392],"domain_scores_gemma":[0.99758965,0.000035850062,0.00055245415,0.00021635552,0.0014680201,0.00013764684],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004912094,0.00038947188,0.00025664832,0.00037259457,0.00031159417,0.0018810949,0.001338734,0.00012219715,0.000108165405],"category_scores_gemma":[0.0007469459,0.00039463007,0.00006777609,0.00055123895,0.00013843803,0.005611336,0.00019282103,0.00044344738,0.00007418508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005955475,0.0003224889,0.0002409881,0.00015914753,0.00003244,0.0000071643212,0.0012877688,0.000007758246,0.5257107,0.3289042,0.0043088105,0.13895899],"study_design_scores_gemma":[0.00041227325,0.00009223782,0.000021892118,0.00037689338,0.00000864872,0.0000540992,0.00012488996,0.7492529,0.12443379,0.1230701,0.0016674446,0.00048484086],"about_ca_topic_score_codex":0.0000043521286,"about_ca_topic_score_gemma":3.3173689e-7,"teacher_disagreement_score":0.7492451,"about_ca_system_score_codex":0.0002066939,"about_ca_system_score_gemma":0.00017544521,"threshold_uncertainty_score":0.9998506},"labels":[],"label_agreement":null},{"id":"W2292685811","doi":"10.1016/j.compeleceng.2016.02.014","title":"Diagnostically lossless coding of X-ray angiography images based on background suppression","year":2016,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; Ministerio de Economía y Competitividad; Federación Española de Enfermedades Raras","keywords":"Lossless compression; Lossy compression; JPEG 2000; Computer science; Computer vision; Artificial intelligence; JPEG; Decoding methods; Segmentation; Coding (social sciences); Angiography; Image resolution; Data compression; Image compression; Image processing; Mathematics; Algorithm; Radiology; Medicine; Image (mathematics)","score_opus":0.007901224675459489,"score_gpt":0.2310615660884465,"score_spread":0.22316034141298702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2292685811","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013985989,0.00021322482,0.99661154,0.00056964596,0.00026536713,0.00013712017,0.0000022863894,0.00076619186,0.000036056867],"genre_scores_gemma":[0.55992544,0.00002164548,0.43988815,0.000091952454,0.000037154354,0.000012136101,6.923709e-7,0.000018735002,0.0000040985747],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982622,0.000039808376,0.00032411722,0.00051804277,0.00036626772,0.0004895211],"domain_scores_gemma":[0.9975759,0.0014722075,0.00010290385,0.000579173,0.00011856614,0.00015128347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018634075,0.00025795586,0.00031410408,0.0004929072,0.000070452195,0.000090318,0.0010316358,0.00008483439,0.0000023548212],"category_scores_gemma":[0.0002611744,0.0001952688,0.00018111721,0.0010032306,0.00006093048,0.00048003142,0.0002040895,0.00018332133,0.0000043294285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006129579,0.0004629452,0.00075300277,0.00020929283,0.00006856491,0.00013654557,0.00006348964,0.017585512,0.71096003,0.020727042,0.003540629,0.24543162],"study_design_scores_gemma":[0.00048321378,0.00031492565,0.0024079487,0.0006706299,0.000009979374,0.0000063622047,2.6513257e-7,0.8798068,0.11469081,0.00096538133,0.00026783234,0.00037584294],"about_ca_topic_score_codex":0.0000011387574,"about_ca_topic_score_gemma":2.2078618e-8,"teacher_disagreement_score":0.8622213,"about_ca_system_score_codex":0.000099880046,"about_ca_system_score_gemma":0.000038886454,"threshold_uncertainty_score":0.79628307},"labels":[],"label_agreement":null},{"id":"W2312478680","doi":"10.1364/is.2010.iwa5","title":"Restoration of Turbulence-Degraded Images Using Pixel Histograms","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Pixel; Histogram; Turbulence; Computer science; Computer vision; Artificial intelligence; Image restoration; Image (mathematics); Image processing; Physics; Meteorology","score_opus":0.019807479208808574,"score_gpt":0.2904304432558699,"score_spread":0.2706229640470613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2312478680","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01418652,0.00005510962,0.983507,0.00021653529,0.00019408828,0.000080214544,3.7339956e-7,0.0005155706,0.0012445976],"genre_scores_gemma":[0.33162212,0.0000025783233,0.6681904,0.00004357304,0.000018662478,0.0000030486501,3.992274e-7,0.0000055181767,0.000113700546],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991911,0.000018294113,0.00020128839,0.00024595502,0.00019298887,0.00015038841],"domain_scores_gemma":[0.99907434,0.000028888506,0.00015130783,0.000522386,0.00018112505,0.000041973704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017224714,0.000096624084,0.000114162634,0.00010553329,0.0000766956,0.00007663124,0.00064513105,0.000055410703,0.000007965303],"category_scores_gemma":[0.000110752444,0.00008571786,0.00003356093,0.00032058786,0.00013078318,0.0011436755,0.00014949232,0.00016659894,0.000003057472],"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":[0.0000013654494,0.000029445415,0.00017834525,0.0000104782775,0.0000012593086,0.0000010507882,0.00010091135,0.00000288971,0.9573977,0.010934079,0.00019154896,0.031150952],"study_design_scores_gemma":[0.00013691386,0.00006400561,0.00034817273,0.000033047883,0.000005189699,0.000031305724,0.000011205199,0.13506095,0.792516,0.06991949,0.0016159718,0.00025771695],"about_ca_topic_score_codex":0.000048009282,"about_ca_topic_score_gemma":0.000007145888,"teacher_disagreement_score":0.3174356,"about_ca_system_score_codex":0.00002333094,"about_ca_system_score_gemma":0.00006817791,"threshold_uncertainty_score":0.3495473},"labels":[],"label_agreement":null},{"id":"W2320813190","doi":"10.1541/ieejeiss.133.908","title":"Sharpness-enhancing Enlargement of Terahertz Images","year":2013,"lang":"en","type":"article","venue":"IEEJ Transactions on Electronics Information and Systems","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bruker (Canada)","funders":"","keywords":"Interpolation (computer graphics); Discrete wavelet transform; Image scaling; Artificial intelligence; Algorithm; Parametric statistics; Wavelet; Noise (video); Image (mathematics); Peak signal-to-noise ratio; Mathematics; SIGNAL (programming language); Computer vision; Inverse; Terahertz radiation; Computer science; Wavelet transform; Image processing; Optics; Physics; Statistics; Geometry","score_opus":0.006772837305226504,"score_gpt":0.23087086721385486,"score_spread":0.22409802990862834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2320813190","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011141642,0.00032372173,0.99677855,0.00017874604,0.00012359006,0.0003147426,0.000004185546,0.00022600287,0.00093630655],"genre_scores_gemma":[0.9656786,0.00018282946,0.03376464,0.00012246612,0.000007113309,0.00013520465,0.0000030952433,0.0000053634863,0.00010073444],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900293,0.00002754358,0.00042563662,0.00011709691,0.00021603947,0.00021073686],"domain_scores_gemma":[0.999268,0.000035038945,0.00018201643,0.00026035006,0.0002080198,0.000046580353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022072507,0.00011442683,0.00014357688,0.00020086665,0.00014062149,0.00027598854,0.00025005802,0.000049496743,0.000010961575],"category_scores_gemma":[0.0000080308355,0.00010343434,0.00003246741,0.00022920783,0.000024950363,0.0033122317,0.0000062151094,0.00015170786,0.000016966633],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017007644,0.00015404286,0.000033705568,0.00075470394,0.00009089747,6.670133e-7,0.003993921,0.0023044199,0.048551053,0.04291814,0.0007865992,0.90039486],"study_design_scores_gemma":[0.0008126101,0.0005355106,0.00013436234,0.00030238647,0.00001732681,0.00007182633,0.0006036674,0.80007327,0.16056155,0.0052647227,0.031032357,0.0005904409],"about_ca_topic_score_codex":0.000030227571,"about_ca_topic_score_gemma":0.0000023532057,"teacher_disagreement_score":0.9645644,"about_ca_system_score_codex":0.00007501923,"about_ca_system_score_gemma":0.000060025934,"threshold_uncertainty_score":0.42179298},"labels":[],"label_agreement":null},{"id":"W2353332002","doi":"","title":"A Fast Sub-Pixel Motion Estimation Algorithm Based on Best Position Calculation","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Pixel; Position (finance); Interpolation (computer graphics); Computer science; Algorithm; Motion estimation; Computer vision; Artificial intelligence; Data compression; Exponential function; Computation; Image (mathematics); Mathematics","score_opus":0.010078962172906597,"score_gpt":0.27459481097120286,"score_spread":0.26451584879829626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2353332002","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00040974253,0.000010990097,0.99555224,0.0018244599,0.00005085795,0.00017914751,0.0000013851648,0.0009180609,0.0010531116],"genre_scores_gemma":[0.19781779,0.0000013359098,0.80133647,0.0005751263,0.000045007368,0.000024128898,0.000018585974,0.000009309885,0.00017224922],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895924,0.000037514612,0.00019293034,0.0003386636,0.00029972676,0.00017189964],"domain_scores_gemma":[0.9993401,0.000033010572,0.000099605604,0.00033799274,0.00013770502,0.000051577652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016022386,0.00013209925,0.000086184526,0.00018421236,0.00014509226,0.00016695737,0.0002388026,0.00006465604,0.000005680125],"category_scores_gemma":[0.000020419715,0.00012930467,0.00003532054,0.00032809062,0.000023533456,0.0016650782,0.00004095842,0.00009744664,0.000102880455],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023034627,0.00009707608,0.0000074353547,0.0000044514372,8.408783e-7,8.751682e-7,0.00003116341,0.01229385,0.007720647,0.0017015394,0.00009315067,0.97804666],"study_design_scores_gemma":[0.00016028628,0.00008706463,0.00014779138,0.00003642443,0.0000031866919,0.00000691493,0.00000145787,0.92384773,0.073572055,0.0018605861,0.0001386998,0.0001378233],"about_ca_topic_score_codex":0.000007382529,"about_ca_topic_score_gemma":0.0000023971882,"teacher_disagreement_score":0.97790885,"about_ca_system_score_codex":0.00019380965,"about_ca_system_score_gemma":0.00003173986,"threshold_uncertainty_score":0.5272891},"labels":[],"label_agreement":null},{"id":"W2379636349","doi":"","title":"A New Method Based on Boundary Processing for Image Restoration","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ringing; Ringing artifacts; Image restoration; Computer science; Wiener filter; Image (mathematics); Computer vision; Image processing; Artificial intelligence; Filter (signal processing); Boundary (topology); A priori and a posteriori; Process (computing); Image quality; Wiener deconvolution; Algorithm; Mathematics; Deconvolution; Blind deconvolution","score_opus":0.020748612589801197,"score_gpt":0.33322538350491604,"score_spread":0.31247677091511483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2379636349","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000070252577,0.00010215861,0.99553543,0.0018884877,0.000013504305,0.0009993825,0.000004512308,0.0010513732,0.00039812972],"genre_scores_gemma":[0.0005518402,0.0000026365171,0.9965481,0.0015368882,0.00018210716,0.0009188251,0.000022677612,0.000028118628,0.00020879526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986343,0.000032055217,0.00027111807,0.0006254435,0.00017346513,0.00026362814],"domain_scores_gemma":[0.998791,0.00011838259,0.00016265763,0.0005836556,0.0002427681,0.00010154658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017834258,0.00019102493,0.00016458821,0.00019139252,0.00064980093,0.0002664904,0.0008977184,0.00006588828,0.0000019644085],"category_scores_gemma":[0.0000037027721,0.00019924824,0.00007441052,0.0005650402,0.00005403134,0.00074858137,0.00011402118,0.00013866783,0.000024339939],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000104406645,0.00012325167,0.000007815789,0.000053911135,0.000003852761,0.000002479743,0.00030545768,0.00020507228,0.03732422,0.003802591,0.02095346,0.93720746],"study_design_scores_gemma":[0.0004179773,0.00008057612,0.000069429196,0.000037603346,0.0000060214056,0.000048813927,0.000001801835,0.5026704,0.03644226,0.024827817,0.43509728,0.00030003276],"about_ca_topic_score_codex":0.0000045196734,"about_ca_topic_score_gemma":7.333793e-7,"teacher_disagreement_score":0.9369074,"about_ca_system_score_codex":0.00010181272,"about_ca_system_score_gemma":0.00045456362,"threshold_uncertainty_score":0.8125107},"labels":[],"label_agreement":null},{"id":"W2405113653","doi":"10.1109/icassp.2016.7471803","title":"Medical image super-resolution with non-local embedding sparse representation and improved IBP","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Artificial intelligence; Computer science; Iterative reconstruction; Sparse approximation; Benchmark (surveying); Embedding; Image resolution; Computer vision; Noise (video); Residual; Projection (relational algebra); Image (mathematics); Representation (politics); Iterative method; Face (sociological concept); Similarity (geometry); Pattern recognition (psychology); Algorithm","score_opus":0.010137329273323769,"score_gpt":0.29236309504821184,"score_spread":0.28222576577488806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405113653","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042490773,0.000022721913,0.9914664,0.0028027918,0.000043700464,0.00012506977,4.1735873e-7,0.00051464693,0.0007751583],"genre_scores_gemma":[0.36657938,0.000016155429,0.6329927,0.00015482411,0.000025705169,0.00001810225,4.782632e-7,0.000008070891,0.00020462232],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987754,0.000031145777,0.00017104342,0.00045957573,0.00031841593,0.00024441967],"domain_scores_gemma":[0.9992252,0.00009396289,0.000059547125,0.00037116467,0.0001170267,0.00013307795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025047964,0.00012059578,0.000120797435,0.00007425984,0.000099044526,0.00010793583,0.0003650176,0.00006293119,0.000023531966],"category_scores_gemma":[0.00016585032,0.00007148819,0.000016803107,0.00018976443,0.00024730456,0.0018145264,0.00029270008,0.0000864708,0.000010133366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054650158,0.0000703348,0.00070155674,0.000028932029,0.000013245517,0.000082683626,0.00034160414,0.0000026668972,0.29612753,0.004813136,0.0014403738,0.6963233],"study_design_scores_gemma":[0.00091836206,0.00018775111,0.00071026315,0.00016009896,0.000005745792,0.00020727294,0.000047959224,0.8715178,0.11877411,0.006953648,0.0002407066,0.00027633065],"about_ca_topic_score_codex":0.0000419725,"about_ca_topic_score_gemma":0.00001760723,"teacher_disagreement_score":0.8715151,"about_ca_system_score_codex":0.000047457135,"about_ca_system_score_gemma":0.00007852885,"threshold_uncertainty_score":0.2915204},"labels":[],"label_agreement":null},{"id":"W2496000821","doi":"10.1109/i2mtc.2016.7520581","title":"A joint dictionary-based method for single image super-resolution","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Superresolution; Joint (building); Image (mathematics); Resolution (logic); Pattern recognition (psychology); Image resolution; Dictionary learning; Computer vision; Iterative reconstruction","score_opus":0.033733501843249386,"score_gpt":0.3093393077512996,"score_spread":0.27560580590805017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2496000821","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000012843699,0.000030968084,0.98983824,0.0070981802,0.00009001708,0.00021416103,0.0000047629683,0.0013052307,0.0014056053],"genre_scores_gemma":[0.01072355,0.0000012878921,0.98799115,0.0005844602,0.00004058324,0.00011428939,0.0000013773632,0.000013335244,0.00052995037],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989913,0.000041359148,0.00018206336,0.00038263566,0.0001536405,0.00024901517],"domain_scores_gemma":[0.9990624,0.00018944225,0.00006334111,0.00041342367,0.00021384333,0.00005753266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032839904,0.00011421771,0.000114395574,0.000106142215,0.0001253914,0.00008207731,0.00038064815,0.00004363734,0.000020082023],"category_scores_gemma":[0.00017476242,0.00007592242,0.00007251558,0.00017173293,0.000053140167,0.0010404002,0.00011401026,0.000036375055,0.0000178066],"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":[0.0000070727106,0.0000602154,0.000004584914,0.000010474734,0.0000022301008,0.0000012911196,0.000015854208,0.0000026887378,0.8657583,0.01337561,0.002808913,0.11795276],"study_design_scores_gemma":[0.00039477475,0.00017465214,0.000023473773,0.000043528682,0.0000033738697,0.000013303517,0.0000022448987,0.2260671,0.6846051,0.07833781,0.010161777,0.00017281399],"about_ca_topic_score_codex":0.0000063001457,"about_ca_topic_score_gemma":0.0000014002401,"teacher_disagreement_score":0.22606441,"about_ca_system_score_codex":0.00011409923,"about_ca_system_score_gemma":0.00006712538,"threshold_uncertainty_score":0.30960265},"labels":[],"label_agreement":null},{"id":"W2508963293","doi":"10.1109/icip.2016.7532845","title":"Multiframe blind deconvolution of passive millimeter wave images using variational dirichlet blur kernel estimation","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Science North","funders":"","keywords":"Deconvolution; Blind deconvolution; Artificial intelligence; Image restoration; Computer vision; Kernel (algebra); Computer science; Kernel density estimation; Normalization (sociology); Noise (video); Pattern recognition (psychology); Mathematics; Algorithm; Image (mathematics); Image processing","score_opus":0.03009288177130991,"score_gpt":0.2958867193952821,"score_spread":0.2657938376239722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508963293","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005647193,0.000037604517,0.99312633,0.0005540791,0.000089518406,0.00014862363,0.000007824579,0.00024658343,0.0001422568],"genre_scores_gemma":[0.36083502,0.0000049198907,0.638965,0.000061921906,0.000019223484,0.000008287781,0.000001750654,0.000007164143,0.00009672773],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998803,0.000046633486,0.00032646715,0.00035111245,0.0002690494,0.00020370587],"domain_scores_gemma":[0.99872214,0.00022412617,0.00028481332,0.0003466582,0.00037296917,0.000049311082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017966708,0.000140743,0.00015303813,0.00016636551,0.00007870207,0.0000635784,0.00032526537,0.00007524179,0.00003049127],"category_scores_gemma":[0.0003016046,0.00010005242,0.000051486153,0.00024716527,0.00010251721,0.0018186352,0.0001713031,0.000059449954,0.000012697537],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027815782,0.00015240692,0.00055208046,0.000036039964,0.000039726077,0.000005541636,0.00026286146,0.00027062665,0.72479546,0.015894964,0.00038184103,0.2575806],"study_design_scores_gemma":[0.000406949,0.000035058074,0.0019632156,0.0000692886,0.000009240923,0.000015100487,0.0000026637347,0.69067097,0.24813592,0.05850101,0.000025511406,0.00016506964],"about_ca_topic_score_codex":0.000015828555,"about_ca_topic_score_gemma":7.6214536e-7,"teacher_disagreement_score":0.69040036,"about_ca_system_score_codex":0.00011038512,"about_ca_system_score_gemma":0.00008683869,"threshold_uncertainty_score":0.40800193},"labels":[],"label_agreement":null},{"id":"W2519597608","doi":"10.1007/978-3-319-46487-9_45","title":"Learning High-Order Filters for Efficient Blind Deconvolution of Document Photographs","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Deblurring; Computer science; Deconvolution; Computer vision; Blind deconvolution; Artificial intelligence; Kernel (algebra); Scale (ratio); Motion blur; Image restoration; Computer graphics (images); Image (mathematics); Image processing; Algorithm","score_opus":0.011231712334776505,"score_gpt":0.2663833764137624,"score_spread":0.25515166407898593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2519597608","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008951389,0.00035260525,0.9969727,0.00028498156,0.0008414779,0.000729461,0.000005470418,0.000278613,0.0004452033],"genre_scores_gemma":[0.086680934,0.000041319137,0.9126462,0.0002146372,0.00012231486,0.000043523454,0.0000033326899,0.000035970636,0.00021180871],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99647534,0.000030593907,0.00063160324,0.0014400876,0.0007710261,0.00065135956],"domain_scores_gemma":[0.99697757,0.00059441547,0.00065072434,0.0009914042,0.0006702891,0.00011561312],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009464217,0.00048257588,0.00053765933,0.0011074056,0.0002576917,0.00023658591,0.0025190515,0.00024962693,0.000010398168],"category_scores_gemma":[0.0002187322,0.00040305714,0.00015631379,0.0006342382,0.00086279423,0.0005391605,0.0009899852,0.00045892925,0.0000055897062],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028449032,0.000037720045,0.000015888201,0.00012315577,0.000017183438,0.000007903204,0.0005745606,0.028787976,0.0053643654,0.019342303,0.0000096927715,0.9456908],"study_design_scores_gemma":[0.0009122101,0.00065060623,0.000016703987,0.0012861066,0.000015816335,0.000022141274,2.9601856e-7,0.4991383,0.04444839,0.45171797,0.00092535815,0.0008660928],"about_ca_topic_score_codex":0.000007857554,"about_ca_topic_score_gemma":0.0000063568687,"teacher_disagreement_score":0.9448247,"about_ca_system_score_codex":0.00030180224,"about_ca_system_score_gemma":0.00042939687,"threshold_uncertainty_score":0.9998421},"labels":[],"label_agreement":null},{"id":"W2524652555","doi":"10.1145/2964284.2967204","title":"INRS Audiovisual Quality Dataset","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Quality (philosophy); Speech recognition; Artificial intelligence","score_opus":0.039935265467554146,"score_gpt":0.3779546367234578,"score_spread":0.33801937125590364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2524652555","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022137555,0.000019467569,0.99473834,0.00215922,0.00004873194,0.00004137596,0.000034200242,0.0008106064,0.0019266951],"genre_scores_gemma":[0.06972512,0.000007777259,0.92847043,0.000969869,0.000023577148,0.000009325554,0.000007943591,0.0000050520493,0.00078089215],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991616,0.000041184718,0.00015015625,0.00029975284,0.00016863146,0.00017864833],"domain_scores_gemma":[0.99903965,0.00009650655,0.000055534634,0.0007060627,0.000046255187,0.000056010886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003057655,0.00007722493,0.00008460234,0.000039684557,0.00005238003,0.00006274654,0.00089554663,0.000025136454,0.000029446353],"category_scores_gemma":[0.00017591177,0.000045538145,0.000016892693,0.00014319058,0.00006231688,0.0013448264,0.00045661995,0.000036781756,0.00016333426],"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":[0.0000052556534,0.000063122374,0.00063912245,0.000011891009,0.0000063289503,0.000008746332,0.00003627276,6.26177e-8,0.10722214,0.25552323,0.13721094,0.49927288],"study_design_scores_gemma":[0.00091878354,0.00018599522,0.004704155,0.00009143848,0.0000044752046,0.000043243213,0.000011969587,0.0021941995,0.3846444,0.23022495,0.3759375,0.0010389294],"about_ca_topic_score_codex":0.000010192487,"about_ca_topic_score_gemma":0.0000021888056,"teacher_disagreement_score":0.49823397,"about_ca_system_score_codex":0.000028943918,"about_ca_system_score_gemma":0.00003807317,"threshold_uncertainty_score":0.2099386},"labels":[],"label_agreement":null},{"id":"W2542778656","doi":"10.1109/acssc.2012.6489325","title":"Regularization function for video super-resolution using auxiliary high resolution still images","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Regularization (linguistics); Computer vision; Artificial intelligence; Image resolution; Resolution (logic); Superresolution; Low resolution; High resolution; Dual mode; Process (computing); Sub-pixel resolution; Image (mathematics); Image processing; Digital image processing; Remote sensing; Electronic engineering; Engineering","score_opus":0.027416832172040065,"score_gpt":0.28417052085921063,"score_spread":0.2567536886871706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2542778656","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012147104,0.000383093,0.99565154,0.00037532445,0.0005653788,0.00039391115,0.000004041091,0.0011803404,0.00023164989],"genre_scores_gemma":[0.25074157,0.0000057736947,0.74837905,0.000250956,0.00020785589,0.00004400537,0.00002053806,0.00001928098,0.00033095977],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998516,0.00007197973,0.0002869084,0.0003994314,0.00025861643,0.0004670534],"domain_scores_gemma":[0.9988886,0.000069948524,0.00015266432,0.0004831381,0.00031520342,0.00009040428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057703693,0.00018046243,0.00014700482,0.00018983545,0.0002970872,0.00014662392,0.000329548,0.00010224073,0.000009537353],"category_scores_gemma":[0.00021806733,0.00017667394,0.000058117388,0.00043306744,0.00006317661,0.005018316,0.00018521355,0.000100909434,0.000008687709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014891004,0.0003465642,0.0010432348,0.0001814981,0.000034303364,0.0000013341495,0.00040905128,0.0016493495,0.60636413,0.30718824,0.009629312,0.07300407],"study_design_scores_gemma":[0.00042064322,0.00014128603,0.0019034595,0.00006158263,0.000038290873,0.000027641772,0.000020674523,0.79864156,0.09995381,0.091529064,0.0068323896,0.00042960665],"about_ca_topic_score_codex":0.000044912595,"about_ca_topic_score_gemma":0.0000018579501,"teacher_disagreement_score":0.7969922,"about_ca_system_score_codex":0.0002291575,"about_ca_system_score_gemma":0.00006044682,"threshold_uncertainty_score":0.7204554},"labels":[],"label_agreement":null},{"id":"W2550025198","doi":"10.15353/vsnl.v1i1.39","title":"A Discretize-then-Optimize Approach to Super-Resolution Reconstruction and Motion Estimation","year":2015,"lang":"en","type":"article","venue":"Vision Letters","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Universities Space Research Association","keywords":"Discretization; Resolution (logic); Iterative reconstruction; Motion (physics); Image (mathematics); Motion estimation; Algorithm; Image resolution; Inverse; Artificial intelligence; Mathematics; Computer vision; Process (computing); Computer science; Mathematical analysis; Geometry","score_opus":0.01935551639781393,"score_gpt":0.26872906371375244,"score_spread":0.2493735473159385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2550025198","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015739288,0.000035641042,0.97681135,0.006271869,0.00015788221,0.00023870429,8.1379756e-7,0.00049911474,0.0002453657],"genre_scores_gemma":[0.15127851,0.000003077211,0.84780955,0.0008114621,0.00003343989,0.00003043182,0.0000051464835,0.000009870074,0.000018500712],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883085,0.00007627817,0.00019439444,0.00044273044,0.00026691856,0.00018883892],"domain_scores_gemma":[0.999335,0.000023050894,0.00007920343,0.00033883375,0.00008555307,0.00013838992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038336767,0.0001334313,0.00012217507,0.00020722585,0.00011631444,0.00023167339,0.0002599009,0.000051387637,7.325738e-7],"category_scores_gemma":[0.00014761003,0.00012400636,0.000022910794,0.000353992,0.00006687681,0.0016794361,0.0001756292,0.00010510884,0.000013540959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039442828,0.00007240062,0.0001519664,0.000025726858,0.0000059302843,0.0000024128672,0.0018307824,0.010732463,0.049274646,0.0031607065,0.006463824,0.9282397],"study_design_scores_gemma":[0.00030453812,0.000077155004,0.00035559759,0.00005260118,0.000004385505,0.00007586633,0.000037960246,0.99277616,0.0019876377,0.003720191,0.0004098419,0.00019807868],"about_ca_topic_score_codex":0.0000134296015,"about_ca_topic_score_gemma":2.286497e-7,"teacher_disagreement_score":0.9820437,"about_ca_system_score_codex":0.00011269211,"about_ca_system_score_gemma":0.00001758291,"threshold_uncertainty_score":0.50568324},"labels":[],"label_agreement":null},{"id":"W2558385494","doi":"10.1109/globalsip.2017.8308670","title":"Robust multi-frame super-resolution with adaptive norm choice and difference curvature based BTV regularization","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Regularization (linguistics); Norm (philosophy); Mathematics; Algorithm; Noisy data; Minification; Curvature; Fidelity; Quadratic equation; Computer science; Artificial intelligence; Mathematical optimization","score_opus":0.04348871914483703,"score_gpt":0.26878746081821603,"score_spread":0.225298741673379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558385494","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011489112,0.00007141064,0.9963416,0.0012837286,0.000040672476,0.00020740202,0.000002106124,0.00048431227,0.00041987063],"genre_scores_gemma":[0.30167165,0.00000471064,0.6974661,0.0002117838,0.000018454946,0.000018421477,0.0000030107938,0.00000970589,0.0005961646],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988784,0.00003047737,0.000120196564,0.00051439967,0.00022350858,0.00023298347],"domain_scores_gemma":[0.99854463,0.000053478107,0.00016880425,0.0008959571,0.00025798392,0.00007914404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011229073,0.00018329595,0.00014671881,0.000066208464,0.0005780726,0.0005571048,0.0008371501,0.00010218879,0.0000025142094],"category_scores_gemma":[0.00020946181,0.00014044791,0.000016677512,0.00012032118,0.00021801432,0.001796167,0.00028600026,0.00020396234,0.0000016833105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039078004,0.0017061762,0.18108,0.0005686992,0.0001626996,0.00010518697,0.0032440673,0.004213325,0.14737734,0.21252775,0.0019470385,0.44667694],"study_design_scores_gemma":[0.00048470002,0.00012499711,0.07251055,0.000114023074,0.000007424825,0.000006931813,0.0000059654712,0.9183619,0.005844428,0.0021470138,0.00014040941,0.00025164953],"about_ca_topic_score_codex":0.00010169446,"about_ca_topic_score_gemma":0.0001621119,"teacher_disagreement_score":0.91414857,"about_ca_system_score_codex":0.000046002937,"about_ca_system_score_gemma":0.000069780595,"threshold_uncertainty_score":0.57272995},"labels":[],"label_agreement":null},{"id":"W2560998940","doi":"10.1109/crv.2016.51","title":"Genaralizing Generative Models: Application to Image Super-Resolution","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Receptive field; Computer science; Boltzmann machine; Generative model; Markov random field; Artificial intelligence; Restricted Boltzmann machine; Convolutional neural network; Scaling; Algorithm; Pattern recognition (psychology); Field (mathematics); Image (mathematics); Generative grammar; Artificial neural network; Mathematics; Image segmentation","score_opus":0.02459683277539483,"score_gpt":0.2908158909320845,"score_spread":0.26621905815668967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560998940","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019127836,0.000043715016,0.9908649,0.00502646,0.000028727836,0.00020684535,0.0000011435751,0.00097324816,0.002663706],"genre_scores_gemma":[0.10970877,0.000009861412,0.88896763,0.00070282083,0.000036702757,0.00011734314,5.8567576e-7,0.00000911376,0.00044718047],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990285,0.000026447658,0.00014823685,0.0004142842,0.00016101048,0.00022152718],"domain_scores_gemma":[0.99919784,0.000026675238,0.000039503044,0.0004881635,0.00017132667,0.00007646637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014191898,0.00010658905,0.0000837764,0.00008838396,0.00010769321,0.000093686314,0.0005775781,0.00003270606,0.00000503594],"category_scores_gemma":[0.000033251006,0.000072414645,0.000023767116,0.000257236,0.000032402488,0.0021035438,0.00027318246,0.00003480961,0.00010418904],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000220915,0.000014306893,0.0000045475654,0.0000024557203,0.0000016345998,7.563285e-7,0.00018757406,0.00007197725,0.7398831,0.128111,0.0017021734,0.1300182],"study_design_scores_gemma":[0.00009436605,0.000036289508,0.000015140287,0.000019181078,0.0000011749132,0.000005496324,0.000006529838,0.51387495,0.29157975,0.1918285,0.0023496342,0.00018898009],"about_ca_topic_score_codex":0.0000150483675,"about_ca_topic_score_gemma":0.000004733199,"teacher_disagreement_score":0.51380295,"about_ca_system_score_codex":0.00010608129,"about_ca_system_score_gemma":0.000029950244,"threshold_uncertainty_score":0.29529834},"labels":[],"label_agreement":null},{"id":"W2567338558","doi":"10.1109/crv.2016.29","title":"Image Restoration via Deep-Structured Stochastically Fully-Connected Conditional Random Fields (DSFCRFs) for Very Low-Light Conditions","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Conditional random field; Image restoration; Artificial intelligence; Computer science; Noise (video); Image (mathematics); Computer vision; Random field; Set (abstract data type); Image processing; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.007272637217426041,"score_gpt":0.2655184055636998,"score_spread":0.2582457683462738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567338558","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00040514962,0.000018693978,0.99209815,0.005242036,0.0002927636,0.00053474976,0.000046939775,0.00093338423,0.00042814846],"genre_scores_gemma":[0.35268733,0.0000015679884,0.64598024,0.00066064217,0.00012108144,0.00022575982,0.00005660374,0.000015956275,0.00025084158],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99844337,0.000056213532,0.00037399036,0.0005117526,0.00027349454,0.00034119096],"domain_scores_gemma":[0.9980426,0.0006272412,0.00015455493,0.00048124924,0.00057315046,0.00012124386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017335442,0.00021576993,0.00023233633,0.00014817862,0.0002885268,0.00016794715,0.00059201464,0.00014273841,0.0002003924],"category_scores_gemma":[0.00058710366,0.00015585174,0.00009851759,0.00022263445,0.00015552973,0.0017102489,0.000120448705,0.00010958832,0.000038875234],"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":[0.00020365804,0.00009223683,0.000009133851,0.000043245007,0.000040357412,0.00001356533,0.00010315611,0.00001678595,0.799797,0.17556971,0.016459677,0.0076514897],"study_design_scores_gemma":[0.0032723276,0.00020582767,0.00022253384,0.000069308524,0.000020596512,0.000048429298,0.000005780003,0.07910898,0.122270994,0.79335403,0.00096261466,0.00045857087],"about_ca_topic_score_codex":0.0000014172264,"about_ca_topic_score_gemma":0.000015329279,"teacher_disagreement_score":0.677526,"about_ca_system_score_codex":0.0000846977,"about_ca_system_score_gemma":0.000119542994,"threshold_uncertainty_score":0.63554496},"labels":[],"label_agreement":null},{"id":"W2575400867","doi":"10.3934/ipi.2017004","title":"Reducing spatially varying out-of-focus blur from natural image","year":2017,"lang":"en","type":"article","venue":"Inverse Problems and Imaging","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Focus (optics); Computer science; Kernel (algebra); Image (mathematics); Image restoration; Frame (networking); Algorithm; Artificial intelligence; Computer vision; Mathematical optimization; Image processing; Mathematics; Optics","score_opus":0.021723315658485953,"score_gpt":0.2742425411141519,"score_spread":0.25251922545566596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2575400867","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00965795,0.0010392568,0.98420006,0.0015268949,0.00057870714,0.00018202724,0.0000040566147,0.0003538202,0.0024572138],"genre_scores_gemma":[0.47283715,0.000042613225,0.52693844,0.000080743055,0.000049993443,0.0000058941964,9.562585e-7,0.0000123406335,0.000031865533],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986777,0.000024712974,0.00028682046,0.0005113048,0.00019638012,0.00030312594],"domain_scores_gemma":[0.9984799,0.000044296292,0.00042186448,0.0008298899,0.0001484914,0.00007555665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024107791,0.0001912685,0.00022936397,0.0000938993,0.00048700682,0.0007910305,0.0010278706,0.00003146849,0.000004450537],"category_scores_gemma":[0.00018994228,0.0001823512,0.000049659186,0.000063429405,0.00026186087,0.003599753,0.0009723343,0.00024558636,0.0000060296566],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064376954,0.000027338234,0.0022798614,0.00007535962,0.000019765293,0.000047266858,0.0027592997,0.000014896916,0.7081831,0.0007109698,0.00022189801,0.28565383],"study_design_scores_gemma":[0.00070190564,0.000026979873,0.00082291925,0.00080726185,0.000021775126,0.000019997336,0.000030117795,0.7365271,0.16484645,0.094634786,0.0010090099,0.00055172195],"about_ca_topic_score_codex":0.0011526723,"about_ca_topic_score_gemma":0.000027821963,"teacher_disagreement_score":0.7365122,"about_ca_system_score_codex":0.00003005919,"about_ca_system_score_gemma":0.00005358057,"threshold_uncertainty_score":0.7627926},"labels":[],"label_agreement":null},{"id":"W2598009569","doi":"10.1016/j.image.2017.03.016","title":"Deblurring of motion blurred images using histogram of oriented gradients and geometric moments","year":2017,"lang":"en","type":"article","venue":"Signal Processing Image Communication","topic":"Advanced Image Processing 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":"Concordia University","funders":"","keywords":"Deblurring; Artificial intelligence; Computer vision; Histogram; Moment (physics); Computer science; Image restoration; Point spread function; Image (mathematics); Mathematics; Image processing","score_opus":0.03243007458686936,"score_gpt":0.3240563850906596,"score_spread":0.29162631050379023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2598009569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047587216,0.0017503727,0.94992924,0.00007253959,0.000026568363,0.00018394837,0.000002877295,0.00013583792,0.00031142743],"genre_scores_gemma":[0.5495201,0.000062846564,0.45037952,0.0000048401052,0.0000040153604,0.0000065369063,0.0000032463774,0.000010723567,0.000008190145],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985099,0.00009421442,0.00049678143,0.0003373977,0.000335743,0.00022598788],"domain_scores_gemma":[0.9964693,0.00006153508,0.0013863901,0.0013088736,0.00071253645,0.000061349434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058724225,0.00017993197,0.00028885182,0.00043021486,0.00071634183,0.00028654642,0.0017146387,0.00006344518,0.0000014396053],"category_scores_gemma":[0.00035641005,0.0001898337,0.000046211084,0.0004979759,0.00057167304,0.0033994277,0.0010485841,0.00018752865,4.108696e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023019735,0.0003013081,0.007732599,0.0005143802,0.000019798186,0.0000016533245,0.00077830657,0.000025514031,0.33099672,0.00033119632,0.000011548079,0.65926397],"study_design_scores_gemma":[0.0011528234,0.0001745659,0.019460067,0.0016816634,0.00007198448,0.000026052148,0.00009831887,0.531588,0.42543295,0.019701269,0.00006741799,0.0005449026],"about_ca_topic_score_codex":0.00010584506,"about_ca_topic_score_gemma":0.0000012639422,"teacher_disagreement_score":0.65871906,"about_ca_system_score_codex":0.00009428745,"about_ca_system_score_gemma":0.0000701455,"threshold_uncertainty_score":0.7741194},"labels":[],"label_agreement":null},{"id":"W2599414549","doi":"10.1016/j.mri.2017.03.008","title":"Fast single image super-resolution using estimated low-frequency k-space data in MRI","year":2017,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Advanced Image Processing 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":"Canadian Nautical Research Society","funders":"National Natural Science Foundation of China","keywords":"k-space; Mathematics; Artificial intelligence; Peak signal-to-noise ratio; Robustness (evolution); Image (mathematics); Image resolution; Imaging phantom; Computer vision; Interpolation (computer graphics); Algorithm; Image scaling; Computer science; Image processing; Physics; Optics","score_opus":0.0414991350076261,"score_gpt":0.3246107882026478,"score_spread":0.28311165319502174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2599414549","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00899102,0.009419584,0.9758527,0.0025551817,0.00026108322,0.00033895415,0.000017555203,0.0006114417,0.0019524625],"genre_scores_gemma":[0.17458038,0.00008181681,0.8249794,0.00011795438,0.000058681486,0.000016867221,0.000009204028,0.00004107018,0.00011461639],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99686104,0.00009073913,0.00048453812,0.0012552827,0.00046158972,0.00084680977],"domain_scores_gemma":[0.9949311,0.00006679209,0.0003157969,0.0043769735,0.0001975274,0.000111803085],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00063420425,0.00035349635,0.00033426998,0.00024101026,0.000719088,0.0016502115,0.0051196436,0.00006605282,0.00001531164],"category_scores_gemma":[0.00088215823,0.00038818587,0.00004012013,0.00039772663,0.0005563878,0.0066563506,0.0026937106,0.00036234807,0.000024427969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017911792,0.00024260569,0.029759837,0.00011552723,0.0000022397153,0.00063774706,0.0005208067,0.00009346442,0.36812353,0.0013352719,0.00075034547,0.5984007],"study_design_scores_gemma":[0.0005217248,0.000034314286,0.020728638,0.0007380568,0.000006642972,0.000107942586,0.000022097065,0.9632405,0.0058808657,0.007277106,0.00094252505,0.0004995802],"about_ca_topic_score_codex":0.00072429364,"about_ca_topic_score_gemma":0.000059604787,"teacher_disagreement_score":0.96314704,"about_ca_system_score_codex":0.0002353629,"about_ca_system_score_gemma":0.0001700109,"threshold_uncertainty_score":0.999857},"labels":[],"label_agreement":null},{"id":"W2621540102","doi":"10.1109/tip.2017.2713950","title":"Derivative Kernels: Numerics and Applications","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Algorithm; Mathematics; Hessian matrix; Ripple; Frequency domain; Robustness (evolution); Impulse response; Kernel (algebra); Gaussian; Computer science; Applied mathematics; Mathematical analysis; Power (physics); Discrete mathematics","score_opus":0.020348598558654566,"score_gpt":0.30788852409134476,"score_spread":0.2875399255326902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621540102","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008907893,0.00018437534,0.99626964,0.000731206,0.0000660704,0.00023400232,0.0000041338258,0.0006703258,0.0017511442],"genre_scores_gemma":[0.40972868,0.000056743804,0.58967644,0.00016455037,0.000026372863,0.00017322622,2.3076822e-7,0.000018966122,0.00015478487],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99876463,0.000021420054,0.00020561622,0.0005369443,0.0002019369,0.00026946364],"domain_scores_gemma":[0.9985777,0.000049757116,0.00024892105,0.0008093285,0.00021337296,0.000100910336],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00014140367,0.00020329173,0.00016951072,0.00012538496,0.0020584338,0.0013738227,0.00097158365,0.000062560335,0.0000041799117],"category_scores_gemma":[0.00002291154,0.00020531642,0.000040062187,0.00019900997,0.00036574795,0.0034433135,0.000015696434,0.0002942384,0.000017079705],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051486136,0.000082144754,0.000009850213,0.00006740804,0.000007388131,0.0000048647944,0.00036598544,0.00001879671,0.015499248,0.00030262966,0.00001815802,0.9836184],"study_design_scores_gemma":[0.0010460422,0.00018956096,0.0005059846,0.0005181762,0.00006568432,0.00018794,0.00021640705,0.2920379,0.6157291,0.08332251,0.00472471,0.0014559514],"about_ca_topic_score_codex":0.000008057124,"about_ca_topic_score_gemma":0.000002138021,"teacher_disagreement_score":0.9821624,"about_ca_system_score_codex":0.000049554525,"about_ca_system_score_gemma":0.00009461491,"threshold_uncertainty_score":0.9996629},"labels":[],"label_agreement":null},{"id":"W2712273292","doi":"10.1016/j.eswa.2019.112854","title":"Low resolution face recognition using a two-branch deep convolutional neural network architecture","year":2019,"lang":"en","type":"preprint","venue":"Expert Systems with Applications","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Artificial intelligence; Convolutional neural network; Computer science; Face (sociological concept); Pattern recognition (psychology); Low resolution; Computer vision; Resolution (logic); Facial recognition system; Transformation (genetics); Image resolution; Set (abstract data type); Image (mathematics); Network architecture; High resolution; Geography; Remote sensing","score_opus":0.026240713047599774,"score_gpt":0.2913787050287733,"score_spread":0.2651379919811735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2712273292","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002762253,0.0051854216,0.988813,0.0003648293,0.0005376042,0.0032041152,0.000041257732,0.0012701558,0.0003074122],"genre_scores_gemma":[0.21805556,0.000037157373,0.77599525,0.00022128788,0.0010604582,0.004223866,0.00026352916,0.000071927825,0.000070965456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966592,0.00021249634,0.0006085567,0.0013547626,0.00057551335,0.0005894487],"domain_scores_gemma":[0.9966784,0.00013235961,0.00078079524,0.001736511,0.00052221393,0.00014968454],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030772085,0.0005209044,0.0005194183,0.00020745925,0.0004966148,0.0004686862,0.0013837368,0.00031732174,0.0000032617697],"category_scores_gemma":[0.000021931737,0.0004899434,0.00011476411,0.00064260355,0.00015437925,0.0004721527,0.00079113286,0.00086705724,0.000042849522],"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":[0.000038710827,0.00013024021,0.00006890717,0.0004674642,0.000078854355,0.0000041449953,0.00085864525,0.9694027,0.0026940333,0.0027615593,0.0010299349,0.022464823],"study_design_scores_gemma":[0.0003217599,0.00003910537,0.000030022797,0.0009438876,0.000019442223,0.00020533866,0.00003483541,0.98287624,0.00023044036,0.0113113215,0.0032948428,0.0006927445],"about_ca_topic_score_codex":0.00015287033,"about_ca_topic_score_gemma":0.000014455287,"teacher_disagreement_score":0.21777932,"about_ca_system_score_codex":0.00043737466,"about_ca_system_score_gemma":0.00038930052,"threshold_uncertainty_score":0.9997552},"labels":[],"label_agreement":null},{"id":"W2738579427","doi":"10.1109/cvpr.2017.33","title":"Deep Video Deblurring for Hand-Held Cameras","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":600,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology; University of British Columbia","funders":"King Abdullah University of Science and Technology","keywords":"Deblurring; Motion blur; Computer science; Artificial intelligence; Computer vision; Shake; Frame (networking); Task (project management); Frame rate; Deep learning; Motion (physics); Image (mathematics); Image restoration; Image processing","score_opus":0.02547686489349839,"score_gpt":0.32452000947595433,"score_spread":0.29904314458245596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2738579427","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014834708,0.00009774006,0.9924284,0.0013691393,0.00013827582,0.0001413319,2.2843436e-7,0.00049840426,0.0051781265],"genre_scores_gemma":[0.19743627,0.0000046368996,0.8012679,0.00043809778,0.000046752128,0.00004811654,2.0468653e-7,0.000008991549,0.0007489826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921745,0.0000055760324,0.00011837227,0.00031219778,0.000103063496,0.00024332585],"domain_scores_gemma":[0.99872035,0.000058419133,0.00011082147,0.0009447609,0.00010904843,0.00005661569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014927599,0.00009962328,0.00010951805,0.000041564304,0.00078433886,0.0009074309,0.0015857326,0.000035814315,0.00000559103],"category_scores_gemma":[0.00031588125,0.00008728808,0.000043324493,0.000033888344,0.00008612082,0.0015198571,0.0004425451,0.00006435668,0.000014408169],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000101627775,0.000047546804,0.0011086983,0.000062614134,0.000015012432,0.000011648095,0.00041682954,0.000018491466,0.028718106,0.09817905,0.0040436494,0.86736816],"study_design_scores_gemma":[0.00036690955,0.00007402256,0.0004456361,0.00005630778,0.000004900154,0.000016027703,0.0000079644,0.5516916,0.2623896,0.16550961,0.019093826,0.00034359272],"about_ca_topic_score_codex":0.000028345556,"about_ca_topic_score_gemma":0.000024616082,"teacher_disagreement_score":0.8670246,"about_ca_system_score_codex":0.00002282035,"about_ca_system_score_gemma":0.000030710515,"threshold_uncertainty_score":0.87503767},"labels":[],"label_agreement":null},{"id":"W2758395447","doi":"10.4236/ojmi.2017.74014","title":"Application of Sparse-Coding Super-Resolution to 16-Bit DICOM Images for Improving the Image Resolution in MRI","year":2017,"lang":"en","type":"article","venue":"Open Journal of Medical Imaging","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Bicubic interpolation; Artificial intelligence; DICOM; Computer vision; Image quality; Image resolution; Interpolation (computer graphics); Medical imaging; Pattern recognition (psychology); Linear interpolation; Image (mathematics)","score_opus":0.026731498788496465,"score_gpt":0.35955399791033066,"score_spread":0.3328224991218342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2758395447","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008030029,0.0002945633,0.96437055,0.033624016,0.00017279119,0.00044940258,0.0000017851532,0.000022657197,0.00026125423],"genre_scores_gemma":[0.5029853,0.000032656142,0.49654627,0.0002636494,0.00011853869,0.00002910593,6.025697e-7,0.000011759114,0.000012143983],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978383,0.00009793832,0.000703502,0.00029095766,0.00074792857,0.0003213328],"domain_scores_gemma":[0.9975929,0.00022491606,0.0009190418,0.0006985598,0.0004070044,0.00015752466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005210658,0.00013634503,0.00030524065,0.00018002008,0.00048782365,0.00081636483,0.0049764244,0.00005132498,0.0000046163996],"category_scores_gemma":[0.0029711332,0.00010278956,0.00007811752,0.00017693032,0.00024784156,0.003389002,0.0014687908,0.00040629765,0.0000014674154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008519522,0.000119759265,0.0017474313,0.000096530246,0.000010831528,0.000032050517,0.0006769046,0.00004846775,0.12657325,0.003776201,0.004012984,0.8628204],"study_design_scores_gemma":[0.0019083379,0.00014729238,0.004515588,0.0014004392,0.000032002918,0.00027010083,0.0003600824,0.8933042,0.06623402,0.026932163,0.004544712,0.00035102322],"about_ca_topic_score_codex":0.00026991608,"about_ca_topic_score_gemma":0.000016621201,"teacher_disagreement_score":0.8932558,"about_ca_system_score_codex":0.00017072687,"about_ca_system_score_gemma":0.00023749466,"threshold_uncertainty_score":0.9247517},"labels":[],"label_agreement":null},{"id":"W2771758290","doi":"10.1109/cjece.2017.2751623","title":"Fast Deconvolution for Motion Blur Along the Blurring Paths","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Deconvolution; Deblurring; Computer vision; Image restoration; Blind deconvolution; Fast Fourier transform; Artificial intelligence; Motion blur; Wiener filter; Mathematics; Algorithm; Pixel; Computer science; Point spread function; Wiener deconvolution; Image processing; Image (mathematics)","score_opus":0.00891101261355163,"score_gpt":0.21506457997368322,"score_spread":0.2061535673601316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771758290","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070670326,0.0007991058,0.9912064,0.00055899593,0.00026976073,0.00006314292,4.240974e-7,0.000025261312,0.000009876351],"genre_scores_gemma":[0.75469357,0.000015088223,0.24497859,0.000067283494,0.00023014485,0.0000029208798,1.2113362e-7,0.0000066276802,0.00000565741],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993795,0.000008025383,0.00017734975,0.000115754774,0.00007099233,0.00024836353],"domain_scores_gemma":[0.99927735,0.00007188362,0.00013979035,0.00019158259,0.00012462668,0.00019477596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021589398,0.00008813709,0.000116552335,0.00012722121,0.00038281543,0.00045901514,0.00066864054,0.000034719225,3.4418994e-7],"category_scores_gemma":[0.00013479435,0.00006886059,0.000048915495,0.00007183939,0.00002670694,0.00061548804,0.000042194722,0.00017877664,2.57703e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032924672,0.000008090925,0.0019713948,0.000031599193,0.000029694249,0.000053305946,0.0002489091,0.0031286748,0.0008019301,0.017077474,0.00046178317,0.97618383],"study_design_scores_gemma":[0.00015234176,0.000097785516,0.010268646,0.00006306234,0.000006367271,0.00024419205,9.292262e-7,0.98393095,0.00057251216,0.0025901205,0.0019669835,0.00010610123],"about_ca_topic_score_codex":0.00009217665,"about_ca_topic_score_gemma":0.00009237162,"teacher_disagreement_score":0.9808023,"about_ca_system_score_codex":0.00007087576,"about_ca_system_score_gemma":0.00012226272,"threshold_uncertainty_score":0.44262937},"labels":[],"label_agreement":null},{"id":"W2794276902","doi":"10.3390/rs10030394","title":"Landsat Super-Resolution Enhancement Using Convolution Neural Networks and Sentinel-2 for Training","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada; Environment and Climate Change Canada","funders":"Canadian Space Agency","keywords":"Computer science; Convolutional neural network; Remote sensing; Land cover; Artificial intelligence; Image resolution; Pattern recognition (psychology); Land use; Geology","score_opus":0.04701934527246122,"score_gpt":0.30687620020952155,"score_spread":0.25985685493706034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794276902","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06534573,0.00019412795,0.9334251,0.00026471715,0.0002533689,0.00017303956,2.3047615e-7,0.00028279726,0.00006086995],"genre_scores_gemma":[0.48157185,0.000005952038,0.5180114,0.00017909658,0.00021094065,2.912935e-8,0.000001594589,0.000009598451,0.000009517954],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988696,0.00004141186,0.00020434671,0.00038356267,0.00012256144,0.0003785259],"domain_scores_gemma":[0.9993593,0.000058038877,0.00011111214,0.00022813962,0.00018580345,0.000057601388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030067578,0.00014301192,0.00015531896,0.000080505415,0.00038493777,0.00015876698,0.00012075217,0.0000642031,3.671528e-7],"category_scores_gemma":[0.0000726723,0.00014841468,0.000033910626,0.0001906568,0.00011475589,0.00049842434,0.00013422713,0.00009520431,4.6752103e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002686387,0.0000072405983,0.000018061686,0.000035610137,0.000011986777,0.0000059926538,0.0010127907,0.000682272,0.21690331,0.00022726662,0.00008761678,0.780981],"study_design_scores_gemma":[0.00022657414,0.00005195666,0.000017601351,0.00010729784,0.000009723566,0.00009576182,0.00002459292,0.98893243,0.008013241,0.0020524666,0.0002989575,0.00016938469],"about_ca_topic_score_codex":0.000015531463,"about_ca_topic_score_gemma":0.0000037484222,"teacher_disagreement_score":0.9882502,"about_ca_system_score_codex":0.00007572273,"about_ca_system_score_gemma":0.000028288861,"threshold_uncertainty_score":0.60521746},"labels":[],"label_agreement":null},{"id":"W2795326383","doi":"10.1101/271338","title":"Systematic differences between visually-relevant global and local image statistics of brain MRI and natural scenes","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; National Institute of Neurological Disorders and Stroke; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; U.S. Department of Defense; Eli Lilly and Company; Kaiser Permanente; University of Southern California; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; National Eye Institute; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Artificial intelligence; Image processing; Computer science; Pattern recognition (psychology); Scene statistics; Computer vision; Natural (archaeology); Image (mathematics); Heuristic; Psychology; Geography; Perception","score_opus":0.009927417466777805,"score_gpt":0.25856299296152596,"score_spread":0.24863557549474816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795326383","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08882798,0.0033217976,0.9060092,0.00015665164,0.00029295593,0.0006362323,0.00026269472,0.00049171195,7.742022e-7],"genre_scores_gemma":[0.5623955,0.00012038476,0.43730834,0.000045243625,0.00006713278,0.00003601572,1.4842632e-7,0.000026623466,6.3588914e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99675614,0.00024763073,0.00083940267,0.0011324266,0.00054076454,0.00048365613],"domain_scores_gemma":[0.9966039,0.00035813102,0.00089214527,0.0010984879,0.00081790314,0.0002294115],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00075389334,0.0005968749,0.0010925654,0.00018194791,0.00019284135,0.00073690293,0.0012562505,0.00030574566,0.0000010488159],"category_scores_gemma":[0.0006725396,0.0005468753,0.00005794274,0.00039931832,0.0010632587,0.0005096313,0.0021094696,0.0004448296,0.000003343425],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013172271,0.00077411684,0.108805984,0.28283226,0.0021038237,0.00051082735,0.00049734587,0.0000074284526,0.53435564,0.06762351,0.0017701068,0.00058723585],"study_design_scores_gemma":[0.0019638843,0.0010703127,0.46429217,0.057792377,0.00094006635,0.0000012886401,0.00002872853,0.1657665,0.2963657,0.0058182334,0.00010063593,0.0058600944],"about_ca_topic_score_codex":0.000033035278,"about_ca_topic_score_gemma":0.0000025348404,"teacher_disagreement_score":0.47356752,"about_ca_system_score_codex":0.00016028805,"about_ca_system_score_gemma":0.00044481384,"threshold_uncertainty_score":0.9996983},"labels":[],"label_agreement":null},{"id":"W2798790269","doi":"10.1109/iranianmvip.2017.8342350","title":"Single image super resolution by adaptive K-means clustering","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Cluster analysis; Artificial intelligence; Image (mathematics); Resolution (logic); Image resolution; Pixel; Selection (genetic algorithm); Image quality; Computer vision; Software; Sub-pixel resolution; Pattern recognition (psychology); Vocabulary; Data mining; Image processing; Digital image processing","score_opus":0.028698841231653578,"score_gpt":0.2852037067472876,"score_spread":0.25650486551563406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2798790269","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013539835,0.00006114299,0.9653743,0.0011456119,0.000092865404,0.00008329129,0.0000016006011,0.00072024146,0.03238554],"genre_scores_gemma":[0.1394128,0.000005841866,0.8588806,0.00017668752,0.000028553433,0.000010105151,7.865561e-7,0.000010795782,0.0014738434],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990578,0.000019937272,0.00013070952,0.00036328888,0.00016569592,0.00026251198],"domain_scores_gemma":[0.99876255,0.000021091053,0.00010239611,0.0009505993,0.00010552029,0.00005783652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015072084,0.00011895895,0.00010353263,0.000040003222,0.00051413674,0.0006474308,0.0013129695,0.00004457758,0.00001047868],"category_scores_gemma":[0.00011335952,0.000111030975,0.000031627333,0.000049772367,0.00013643401,0.0032302071,0.0007766957,0.00010656424,0.000034469915],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019156543,0.0001555028,0.0001061632,0.000021069636,0.0000121747535,0.000029014504,0.00059628615,0.00001177576,0.79580855,0.010750451,0.030747553,0.16174227],"study_design_scores_gemma":[0.0002858577,0.0001732738,0.0002063748,0.00007094811,0.0000038790276,0.000023932475,0.000033126074,0.7451604,0.22324021,0.01943195,0.010944088,0.0004259748],"about_ca_topic_score_codex":0.000061190396,"about_ca_topic_score_gemma":0.000021011507,"teacher_disagreement_score":0.7451486,"about_ca_system_score_codex":0.000074258234,"about_ca_system_score_gemma":0.000020725114,"threshold_uncertainty_score":0.62431896},"labels":[],"label_agreement":null},{"id":"W2798821117","doi":"10.1109/cvprw.2018.00120","title":"Large Receptive Field Networks for High-Scale Image Super-Resolution","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Receptive field; Benchmark (surveying); Computer science; Convolutional neural network; Memory footprint; Separable space; Artificial intelligence; Field (mathematics); Convolution (computer science); Image (mathematics); Footprint; Grayscale; Algorithm; Pattern recognition (psychology); Computer vision; Artificial neural network; Mathematics; Cartography; Geography","score_opus":0.01432857631181098,"score_gpt":0.2958067779976101,"score_spread":0.2814782016857991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2798821117","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009776286,0.00018255504,0.99193686,0.0018917246,0.0011513719,0.00081287517,0.000023208477,0.0017756363,0.0021280227],"genre_scores_gemma":[0.017396057,0.00007296391,0.9793575,0.001109079,0.000607239,0.000399163,0.00006293506,0.000038314334,0.00095675007],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975897,0.00006422675,0.0003846896,0.001112534,0.00023223911,0.00061657856],"domain_scores_gemma":[0.99755657,0.0002017104,0.00023448956,0.0013091398,0.00060157065,0.00009649421],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005130565,0.00036921684,0.00038386043,0.00013010939,0.00024564803,0.00039607997,0.0019054504,0.00049446034,0.000057763584],"category_scores_gemma":[0.00018612618,0.0003513868,0.0001540218,0.00019247523,0.00009662033,0.00075250986,0.0031871228,0.0005935446,0.000025425123],"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":[0.00018613516,0.00060889806,0.00014711356,0.00064017595,0.00013644603,0.000016631107,0.0017975479,0.0003012168,0.0041499203,0.060483914,0.814992,0.11653998],"study_design_scores_gemma":[0.00033685207,0.00030082118,0.000047147834,0.00025120936,0.000021245765,0.000005023306,0.00002571535,0.6783243,0.028815309,0.28355438,0.0075733685,0.0007446544],"about_ca_topic_score_codex":0.000060360497,"about_ca_topic_score_gemma":0.000077935656,"teacher_disagreement_score":0.80741864,"about_ca_system_score_codex":0.00014880465,"about_ca_system_score_gemma":0.000118074764,"threshold_uncertainty_score":0.9998938},"labels":[],"label_agreement":null},{"id":"W2803394168","doi":"10.1117/12.2304875","title":"Deep generative adversarial networks for infrared image enhancement","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Artificial intelligence; Face (sociological concept); Computer science; Image (mathematics); Generative adversarial network; Infrared; Computer vision; Superresolution; Resolution (logic); Facial recognition system; Convolutional neural network; Image resolution; Pattern recognition (psychology); Generative grammar; Adversarial system; Deep learning; Optics; Physics","score_opus":0.012969653134476681,"score_gpt":0.2902448258323893,"score_spread":0.27727517269791263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803394168","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014402911,0.00004926394,0.9902156,0.00028982328,0.00039204894,0.0003212042,4.3277777e-7,0.00049726124,0.0082199555],"genre_scores_gemma":[0.010101897,0.000006781714,0.9868987,0.0011468166,0.00047211614,0.00012379204,0.0000033425147,0.000010748609,0.0012357796],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898607,0.000021169197,0.00018312587,0.00038247675,0.00012599785,0.0003011527],"domain_scores_gemma":[0.99906504,0.00005319111,0.00008609078,0.00040581194,0.00033241117,0.00005744431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016120952,0.0001323717,0.0001218884,0.000047913265,0.00020686781,0.00017666344,0.00065575057,0.000049699334,0.000047595757],"category_scores_gemma":[0.00007274113,0.00011679336,0.000041819938,0.00019516243,0.00011872796,0.0009705711,0.000288339,0.0000628588,0.00001978625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022092361,0.00034415143,0.000024593579,0.00004609344,0.000109442015,0.0000120315335,0.0034283774,0.00018906312,0.20843957,0.16234724,0.13146889,0.4933696],"study_design_scores_gemma":[0.00027974322,0.0002318089,0.0000038532407,0.0000072560165,0.0000028816444,0.0000014243399,0.000007591078,0.7595159,0.1900845,0.043317314,0.006390543,0.00015719158],"about_ca_topic_score_codex":0.0000023043845,"about_ca_topic_score_gemma":0.0000048972124,"teacher_disagreement_score":0.7593268,"about_ca_system_score_codex":0.000055289536,"about_ca_system_score_gemma":0.000049432198,"threshold_uncertainty_score":0.47626948},"labels":[],"label_agreement":null},{"id":"W2883638418","doi":"10.1109/tip.2019.2924554","title":"SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Ministry of Science and Technology, Taiwan","keywords":"Face (sociological concept); Adversarial system; Artificial intelligence; Computer science; Pattern recognition (psychology); Identity (music); Generative adversarial network; Facial recognition system; Face hallucination; Computer vision; Mathematics; Image (mathematics); Face detection; Linguistics","score_opus":0.01596444325634168,"score_gpt":0.2962569778494424,"score_spread":0.2802925345931007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883638418","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00034844814,0.00020740827,0.9958479,0.0006643409,0.0007297994,0.0007682937,0.000008657826,0.00088335876,0.0005417877],"genre_scores_gemma":[0.21554329,0.000013660564,0.7832468,0.00033134327,0.00013676453,0.00015935034,0.0000031516117,0.000042671803,0.0005229727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780834,0.000074990414,0.00038212826,0.00077955733,0.0004338317,0.00052114815],"domain_scores_gemma":[0.9984642,0.00016164119,0.00025906443,0.00056295696,0.00046022088,0.00009187265],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004497223,0.00030404917,0.00028870496,0.00020890542,0.0006848593,0.00087815215,0.0010402595,0.00012602241,0.00002607561],"category_scores_gemma":[0.000042244046,0.00032211922,0.00012313781,0.00078971905,0.00007253085,0.0076178676,0.000018270614,0.00036570212,0.00004109434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025461547,0.00050176366,0.000018376066,0.00088705315,0.000102638995,0.000016237585,0.0043823654,0.12498465,0.27978426,0.0020369163,0.0009859245,0.5860452],"study_design_scores_gemma":[0.0007488371,0.00015117158,0.0000075845246,0.00023832859,0.000026641754,0.000011011585,0.000077502016,0.86924356,0.11158203,0.017065145,0.00042147364,0.00042670267],"about_ca_topic_score_codex":0.000009298512,"about_ca_topic_score_gemma":0.0000141969,"teacher_disagreement_score":0.74425894,"about_ca_system_score_codex":0.00017661526,"about_ca_system_score_gemma":0.0001928066,"threshold_uncertainty_score":0.9999231},"labels":[],"label_agreement":null},{"id":"W2888703449","doi":"10.1109/globalsip.2018.8646501","title":"Video Super-Resolution via Dynamic Local Filter Network","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Motion compensation; Compensation (psychology); Convolutional neural network; Autoencoder; Filter (signal processing); Optical flow; Frame (networking); Motion estimation; Pixel; Pattern recognition (psychology); Artificial neural network; Image (mathematics)","score_opus":0.008694242466371956,"score_gpt":0.26338324054411333,"score_spread":0.25468899807774137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888703449","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012154282,0.00009693802,0.99380016,0.0007989797,0.00024833236,0.000084101055,2.3283414e-7,0.0012434134,0.003606301],"genre_scores_gemma":[0.32037413,0.0000029661164,0.6779598,0.0009473993,0.00010671552,0.000009504488,0.0000010809955,0.000008580992,0.000589784],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988691,0.000032958364,0.00016780579,0.00038207794,0.00017579718,0.00037223625],"domain_scores_gemma":[0.9991792,0.000025778088,0.00004088384,0.0005497211,0.00013932315,0.00006513413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001813626,0.0001264234,0.00010556061,0.00004954118,0.00018030843,0.00010806261,0.00075376267,0.00006189576,0.00007135657],"category_scores_gemma":[0.00002038292,0.0001104641,0.000034286484,0.0003642602,0.00020958169,0.000935031,0.0004051515,0.00010752489,0.00024236912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017706707,0.0000917811,0.00022552849,0.000022211849,0.000016239224,0.000020515452,0.0003410616,0.0002475001,0.015350938,0.03073417,0.060181446,0.8927509],"study_design_scores_gemma":[0.000075934826,0.00010537094,0.00026714156,0.000020578851,0.0000018443228,0.000025727935,0.0000022220852,0.9026693,0.0041906685,0.08500346,0.007466146,0.00017160077],"about_ca_topic_score_codex":0.000018100009,"about_ca_topic_score_gemma":0.00003927862,"teacher_disagreement_score":0.90242183,"about_ca_system_score_codex":0.00007690002,"about_ca_system_score_gemma":0.00003330386,"threshold_uncertainty_score":0.45045954},"labels":[],"label_agreement":null},{"id":"W2891639959","doi":"10.1109/icassp.2018.8461664","title":"Edge-Based Loss Function for Single Image Super-Resolution","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Mean squared error; Computer science; Enhanced Data Rates for GSM Evolution; Convolutional neural network; Image (mathematics); Pixel; Convolution (computer science); Image restoration; Image quality; Function (biology); Image resolution; Salient; Computer vision; Pattern recognition (psychology); Artificial neural network; Superresolution; Mathematics; Image processing; Statistics","score_opus":0.02239487850176264,"score_gpt":0.2807992077772113,"score_spread":0.25840432927544865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891639959","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024457817,0.000028028813,0.99304295,0.0009616937,0.00027444598,0.00020159649,0.00000133409,0.0013025842,0.0039427835],"genre_scores_gemma":[0.19888572,3.159776e-7,0.7998762,0.00067091093,0.00014992045,0.000041043735,0.0000035685457,0.000010852618,0.00036145837],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99909633,0.000017754393,0.00014335089,0.0003528336,0.00013467716,0.00025502866],"domain_scores_gemma":[0.99903256,0.000052679654,0.00005400353,0.0004159476,0.00039763516,0.000047191552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018295691,0.00010919453,0.00008660385,0.00008770383,0.00020586258,0.000170577,0.00041504964,0.000050115188,0.000024442037],"category_scores_gemma":[0.000104838015,0.00010009818,0.00004497221,0.00025449388,0.00014486852,0.0011327693,0.00009185711,0.000048246002,0.000051732128],"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":[0.00008194693,0.00023947461,0.000060949722,0.00005501416,0.000006861679,0.0000025060715,0.000098312834,0.000006197909,0.8393642,0.024580931,0.021686692,0.11381694],"study_design_scores_gemma":[0.00038672358,0.0007081203,0.000056079392,0.000024444958,0.0000059262607,0.000006915516,0.0000048060956,0.31982157,0.60700786,0.047414094,0.024336116,0.00022734501],"about_ca_topic_score_codex":0.0000055744435,"about_ca_topic_score_gemma":0.0000050612016,"teacher_disagreement_score":0.31981537,"about_ca_system_score_codex":0.00007294022,"about_ca_system_score_gemma":0.00005730231,"threshold_uncertainty_score":0.40818855},"labels":[],"label_agreement":null},{"id":"W2894983638","doi":"10.1007/s00138-018-0983-2","title":"Two-stage local details restoration framework for face hallucination","year":2018,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Hallucinating; Computer science; Artificial intelligence; Face hallucination; Computer vision; Image (mathematics); Position (finance); Residual; Constraint (computer-aided design); Locality; Face (sociological concept); Stage (stratigraphy); Pattern recognition (psychology); Face detection; Mathematics; Facial recognition system; Algorithm","score_opus":0.016629979871543056,"score_gpt":0.3646864681359201,"score_spread":0.348056488264377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894983638","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009440753,0.00014170703,0.9966126,0.0016861294,0.000027903287,0.00048220888,0.0000074410855,0.0003902368,0.0005573567],"genre_scores_gemma":[0.28606158,0.00001118345,0.71317255,0.00030752798,0.00006324026,0.00022316919,0.000011188795,0.000007665246,0.0001418982],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992443,0.00001931209,0.00015911562,0.00033923952,0.00011377989,0.00012425343],"domain_scores_gemma":[0.99919385,0.00009525861,0.00009944132,0.00039621256,0.00015926806,0.000055954853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019362659,0.00009857005,0.00008363031,0.0000798059,0.00038717611,0.00015827293,0.000300692,0.00004992441,0.000005347792],"category_scores_gemma":[0.000058146816,0.000088069915,0.000019024666,0.0002978444,0.00010537701,0.000454592,0.00012167971,0.00008750928,0.000016218582],"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":[0.0000040041527,0.00003366923,0.000019249224,0.000011136573,0.0000013445672,6.6958194e-8,0.00008628973,0.000017028115,0.0030173021,0.4865282,0.00013543706,0.51014626],"study_design_scores_gemma":[0.0001834455,0.00011884112,0.00018703804,0.000020370571,0.0000038452918,0.0000024766025,0.000016191361,0.70277834,0.006247559,0.22669445,0.063608065,0.00013935461],"about_ca_topic_score_codex":0.000007431803,"about_ca_topic_score_gemma":0.000011844412,"teacher_disagreement_score":0.70276135,"about_ca_system_score_codex":0.000026816433,"about_ca_system_score_gemma":0.000020846592,"threshold_uncertainty_score":0.3591387},"labels":[],"label_agreement":null},{"id":"W2896184058","doi":"10.1109/tip.2018.2874284","title":"High-quality Image Restoration Using Low-Rank Patch Regularization and Global Structure Sparsity","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Fonds de recherche du Québec – Nature et technologies; National Natural Science Foundation of China","keywords":"Image restoration; Regularization (linguistics); Artificial intelligence; Iterative reconstruction; Pixel; Computer science; Residual; Image resolution; Pattern recognition (psychology); Deblurring; Norm (philosophy); Mathematics; Computer vision; Algorithm; Image (mathematics); Image processing","score_opus":0.018579098788112276,"score_gpt":0.3071245329623622,"score_spread":0.28854543417424994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896184058","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05340414,0.00005665577,0.94486725,0.00029535653,0.00026266632,0.00023563087,0.000031350366,0.00078490394,0.00006201288],"genre_scores_gemma":[0.50165045,0.000004859629,0.49812114,0.00012309325,0.00006240298,0.0000049102064,0.0000024853225,0.000014981822,0.000015697678],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99776787,0.0001441949,0.00043821151,0.00081165903,0.0004518217,0.00038626787],"domain_scores_gemma":[0.99826807,0.0000322307,0.00032085518,0.0005610851,0.00069402775,0.00012374503],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031994018,0.00032190498,0.0002779233,0.00015860413,0.0011189771,0.0008703976,0.00046783642,0.00017030307,0.000009944023],"category_scores_gemma":[0.00004122563,0.00033616222,0.00004977324,0.0010095636,0.00045262303,0.0046442263,0.000017141878,0.00030844624,0.0000055566297],"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":[0.00010727575,0.00016648778,0.000076102835,0.00031149216,0.000018701427,0.000013395267,0.0007124997,0.00023336738,0.7773991,0.00057129335,0.000034722438,0.22035551],"study_design_scores_gemma":[0.0006652742,0.00012298458,0.00055473694,0.0003185425,0.000048948907,0.00008845125,0.00004852613,0.35117224,0.6008618,0.04544,0.000020636126,0.0006578906],"about_ca_topic_score_codex":0.00009495468,"about_ca_topic_score_gemma":0.00004411587,"teacher_disagreement_score":0.4482463,"about_ca_system_score_codex":0.00028086637,"about_ca_system_score_gemma":0.00021041393,"threshold_uncertainty_score":0.99990904},"labels":[],"label_agreement":null},{"id":"W2897683730","doi":"10.1145/3240508.3240551","title":"Video Forecasting with Forward-Backward-Net","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Consistency (knowledge bases); Margin (machine learning); Artificial intelligence; Field (mathematics); Machine learning; Deep learning; Mathematics","score_opus":0.0219897296878837,"score_gpt":0.26257731681695434,"score_spread":0.24058758712907063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897683730","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007412145,0.000022791173,0.9637806,0.0004601599,0.000058249254,0.00009551887,2.484786e-7,0.0013066577,0.0335346],"genre_scores_gemma":[0.22495095,0.0000010826155,0.77367145,0.0005913206,0.0000782788,0.000012946802,3.0809846e-7,0.000011909298,0.0006817318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989563,0.000013729631,0.00013822048,0.00037589596,0.00020534846,0.00031047605],"domain_scores_gemma":[0.99906546,0.000044958968,0.00007294194,0.000517455,0.00022853547,0.000070637325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016798447,0.0001304892,0.00011517782,0.00007172425,0.00016449869,0.00017935052,0.0007691234,0.000032762673,0.000025777174],"category_scores_gemma":[0.000069348614,0.00009465334,0.000021528258,0.000392915,0.00013866618,0.0011277453,0.0002831048,0.000086115164,0.00006250767],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052895033,0.00011518302,0.0042694095,0.00007108509,0.00004314179,0.00007812485,0.0017283879,0.000014561814,0.007284764,0.07726935,0.028808631,0.88026446],"study_design_scores_gemma":[0.0007042463,0.001466483,0.00032505544,0.00022284524,0.000012462373,0.00038079417,0.000056338467,0.5237637,0.27312207,0.14705934,0.05190517,0.000981475],"about_ca_topic_score_codex":0.000010105693,"about_ca_topic_score_gemma":0.000015832355,"teacher_disagreement_score":0.879283,"about_ca_system_score_codex":0.000028690896,"about_ca_system_score_gemma":0.000057966623,"threshold_uncertainty_score":0.3859851},"labels":[],"label_agreement":null},{"id":"W2897811496","doi":"10.1109/access.2018.2874442","title":"Compnet: A New Scheme for Single Image Super Resolution Based on Deep Convolutional Neural Network","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Residual; Benchmark (surveying); Convolutional neural network; Deep learning; Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Image (mathematics); Scheme (mathematics); Network architecture; Algorithm; Mathematics; Computer network","score_opus":0.05038327753522,"score_gpt":0.3349360743334052,"score_spread":0.28455279679818524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897811496","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010724148,0.00009428651,0.9949857,0.0012399053,0.0006765767,0.00033362277,0.0000042129313,0.00065970275,0.00093360234],"genre_scores_gemma":[0.2589837,7.74601e-7,0.737711,0.0022388878,0.0009237263,0.00004589776,0.000008953273,0.000022173419,0.000064852204],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983664,0.000046621204,0.00024842584,0.0005435543,0.00029594553,0.00049904693],"domain_scores_gemma":[0.9986557,0.00017223983,0.00013337858,0.00056699425,0.00035135605,0.00012036935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021631902,0.00020002006,0.0001780513,0.00010122432,0.00034874232,0.00045841554,0.0014132278,0.00007994981,0.00002080385],"category_scores_gemma":[0.00011057421,0.00019865796,0.000072626186,0.00046790575,0.00019620392,0.0018229545,0.0001814922,0.00014073393,0.000024718189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013649513,0.0013172604,0.0034102877,0.00027175495,0.000081789076,0.000055987653,0.00051939575,0.01974566,0.32249728,0.032011256,0.50017005,0.11855435],"study_design_scores_gemma":[0.0005458358,0.00029339202,0.00034248937,0.0000598054,0.0000048440374,0.00000765377,7.8501273e-7,0.94787735,0.02408235,0.022018023,0.004510168,0.00025727914],"about_ca_topic_score_codex":0.000019589128,"about_ca_topic_score_gemma":0.000015815993,"teacher_disagreement_score":0.9281317,"about_ca_system_score_codex":0.000117290096,"about_ca_system_score_gemma":0.00013421568,"threshold_uncertainty_score":0.81010365},"labels":[],"label_agreement":null},{"id":"W2898282475","doi":"10.1109/tip.2019.2929865","title":"Convolutional Deblurring for Natural Imaging","year":2019,"lang":"en","type":"preprint","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Deblurring; Deconvolution; Image restoration; Computer science; Artificial intelligence; Computer vision; Blind deconvolution; Kernel (algebra); Point spread function; Image quality; Inpainting; Gaussian blur; Finite impulse response; Image processing; Image (mathematics); Algorithm; Mathematics","score_opus":0.01685368270571917,"score_gpt":0.2999516227149906,"score_spread":0.2830979400092714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898282475","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000072826355,0.0017199045,0.99167365,0.000888196,0.0022457587,0.00095246115,0.000050395745,0.0020705299,0.00032625574],"genre_scores_gemma":[0.37596446,0.000022845994,0.6227163,0.00039915953,0.000107680746,0.00038122508,0.00000996949,0.00007759898,0.0003207637],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964036,0.000058969243,0.00064277765,0.0015578996,0.00056749664,0.00076927233],"domain_scores_gemma":[0.9973343,0.0002150792,0.0004907246,0.001009222,0.0008240127,0.00012663967],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003977293,0.0006773848,0.0005586924,0.00058131426,0.0007219965,0.001367097,0.0018681409,0.00022742702,0.000007819075],"category_scores_gemma":[0.000042162796,0.00073051,0.00036504312,0.00039130283,0.00020430863,0.0024137795,0.000062456835,0.0015926644,0.000029734949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012424629,0.0004088694,0.000014568866,0.0035045866,0.00010998432,0.0000297702,0.00095750584,0.017407414,0.046748307,0.00037119896,0.0008223286,0.92950124],"study_design_scores_gemma":[0.00048776483,0.000030390454,0.0000074935524,0.0011979184,0.00005292018,0.00005921823,0.000025622667,0.91527224,0.06671148,0.01491994,0.00039168895,0.00084330444],"about_ca_topic_score_codex":0.000009453265,"about_ca_topic_score_gemma":0.0000019847896,"teacher_disagreement_score":0.9286579,"about_ca_system_score_codex":0.00046379608,"about_ca_system_score_gemma":0.00086564675,"threshold_uncertainty_score":0.99966955},"labels":[],"label_agreement":null},{"id":"W2901985740","doi":"10.1109/mmsp.2018.8547049","title":"A Dual Path Deep Network for Single Image Super-Resolution Reconstruction","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Bicubic interpolation; Upsampling; Artificial intelligence; Computer science; Deep learning; Residual; Path (computing); Image resolution; Sampling (signal processing); Resolution (logic); Computer vision; Interpolation (computer graphics); Image (mathematics); Algorithm; Pattern recognition (psychology); Linear interpolation","score_opus":0.017397728588019534,"score_gpt":0.26522800607989855,"score_spread":0.24783027749187903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901985740","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00054536556,0.000060246035,0.99349225,0.00036120627,0.00043026125,0.00020439141,0.0000010168526,0.0010354948,0.0038697796],"genre_scores_gemma":[0.042840056,0.000003027116,0.9562196,0.00023930868,0.0004917268,0.000039787814,0.000002454943,0.000011746876,0.00015225599],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898815,0.000025118208,0.00018360802,0.00036340638,0.00011135341,0.00032837575],"domain_scores_gemma":[0.9991861,0.000049860384,0.00007643328,0.00034370786,0.00029337488,0.000050550767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002284723,0.00011251979,0.00010527334,0.000050101782,0.00026838426,0.0001789656,0.0003015875,0.000057706962,0.000016066444],"category_scores_gemma":[0.00009942825,0.000105362094,0.000042229334,0.0002744283,0.00014967432,0.001357524,0.00014183424,0.00006065595,0.000021250578],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033681936,0.000100417354,0.0001213642,0.000030751107,0.000012139784,0.000004424006,0.0003727117,0.000013619834,0.16539058,0.038736813,0.015229978,0.77995354],"study_design_scores_gemma":[0.00033710172,0.00059935707,0.00006645222,0.00005874257,0.00000747943,0.00022601732,0.000026828848,0.6939628,0.07921197,0.21773325,0.007441763,0.00032821443],"about_ca_topic_score_codex":0.000006827065,"about_ca_topic_score_gemma":0.000015180669,"teacher_disagreement_score":0.7796253,"about_ca_system_score_codex":0.00006773024,"about_ca_system_score_gemma":0.000034977016,"threshold_uncertainty_score":0.42965415},"labels":[],"label_agreement":null},{"id":"W2903162342","doi":"10.1109/mmsp.2018.8547090","title":"Non-Local Super Resolution in Ultrasound Imaging","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Interpolation (computer graphics); Imaging phantom; Computer science; Image resolution; Envelope (radar); Iterative reconstruction; Computer vision; Sampling (signal processing); Artificial intelligence; Radio frequency; Ultrasound; Resolution (logic); Algorithm; Image (mathematics); Optics; Physics; Acoustics; Telecommunications","score_opus":0.008153457586948557,"score_gpt":0.27417879458110406,"score_spread":0.2660253369941555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903162342","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018544669,0.00003832407,0.9844851,0.000466397,0.00008113695,0.000057714264,9.8846e-8,0.0004303563,0.012586424],"genre_scores_gemma":[0.513186,0.0000020458017,0.48630276,0.0003525442,0.000029011766,0.0000047114486,2.344888e-7,0.0000038775693,0.00011885061],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920046,0.000014251527,0.00013318862,0.00028751482,0.00012524573,0.00023935664],"domain_scores_gemma":[0.99948955,0.000033266104,0.000023043778,0.00033755408,0.000082538194,0.000034034692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020674027,0.000079107725,0.000070320064,0.00010803896,0.00007260671,0.000099057295,0.0004950795,0.000023976821,0.000016477545],"category_scores_gemma":[0.000047767826,0.00007277317,0.000014740381,0.0003589023,0.0001597035,0.0011756476,0.00016462938,0.00009083117,0.00006334021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031859563,0.0002984801,0.035439804,0.000043559627,0.0000075993494,0.00008780214,0.0041073435,0.000036288147,0.33831155,0.07624213,0.027367385,0.5180262],"study_design_scores_gemma":[0.0003102453,0.00006859558,0.007822631,0.00006889581,0.0000012664934,0.00008592506,0.00007221114,0.7713663,0.13517007,0.08056603,0.0041088425,0.0003589991],"about_ca_topic_score_codex":0.00006925154,"about_ca_topic_score_gemma":0.000035487734,"teacher_disagreement_score":0.77133,"about_ca_system_score_codex":0.000073236406,"about_ca_system_score_gemma":0.00003760295,"threshold_uncertainty_score":0.29676035},"labels":[],"label_agreement":null},{"id":"W2906703443","doi":"10.1049/el.2018.7780","title":"High‐speed motion image deblurring using referenceless image quality assessment","year":2019,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Youth Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Deblurring; Computer vision; Image quality; Artificial intelligence; Image (mathematics); Computer science; Motion (physics); Quality (philosophy); Image processing; Image restoration; Physics","score_opus":0.022397329081412152,"score_gpt":0.3232340976445662,"score_spread":0.300836768563154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906703443","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28898355,0.000055597757,0.7085092,0.0013737843,0.00015472634,0.00020052519,0.0000017344892,0.0005344139,0.0001864633],"genre_scores_gemma":[0.4361671,0.000010015879,0.56285316,0.00087523327,0.00003607186,0.0000063970906,0.000006418708,0.000023814553,0.000021792179],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974033,0.00017903738,0.0004105824,0.000749903,0.00049819506,0.000758974],"domain_scores_gemma":[0.9984425,0.000071590104,0.00031085985,0.0009452743,0.00015058569,0.00007920199],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007174436,0.00028043505,0.00032019135,0.0001663577,0.00017523137,0.00042833723,0.00111782,0.0000736365,0.00001959315],"category_scores_gemma":[0.000037880283,0.00029599812,0.00008661585,0.00044951978,0.00006423985,0.0020721925,0.00034626303,0.0005672507,0.000028040271],"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":[0.000004632598,0.00003449556,0.00026444474,0.00003875529,0.000011829061,0.000006522087,0.00006138877,0.00015525456,0.9809948,0.012923992,0.00008518567,0.0054186615],"study_design_scores_gemma":[0.001102895,0.00017005179,0.0023300268,0.00014293185,0.000023938328,0.00004357866,0.000030228328,0.36350623,0.60286283,0.027870707,0.0005903245,0.0013262873],"about_ca_topic_score_codex":0.000058140435,"about_ca_topic_score_gemma":0.0000037712268,"teacher_disagreement_score":0.37813202,"about_ca_system_score_codex":0.000838335,"about_ca_system_score_gemma":0.00017409949,"threshold_uncertainty_score":0.9999492},"labels":[],"label_agreement":null},{"id":"W2910610030","doi":"10.4208/cicp.2008.v4.p195","title":"Lipschitz and Total-Variational Regularization for Blind Deconvolution","year":2008,"lang":"en","type":"article","venue":"Communications in Computational Physics","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Regularization (linguistics); Deconvolution; Lipschitz continuity; Blind deconvolution; Mathematics; Point spread function; Applied mathematics; Mathematical analysis; Mathematical optimization; Computer science; Algorithm; Computer vision; Artificial intelligence","score_opus":0.06465865481866599,"score_gpt":0.3375472305564713,"score_spread":0.2728885757378053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910610030","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00087574025,0.00025498474,0.99612063,0.0019651814,0.000040901057,0.00029061103,0.0000073663396,0.00015959762,0.00028500703],"genre_scores_gemma":[0.35985422,0.000037097296,0.6397589,0.0000956252,0.000025776597,0.00009167725,0.00010594629,0.0000071668273,0.000023617837],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991323,0.00006286126,0.00026825705,0.00025187805,0.00016038441,0.00012433763],"domain_scores_gemma":[0.99836105,0.0005055321,0.0001453788,0.0006129591,0.00034217446,0.000032885993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017933098,0.0001033705,0.00011488002,0.000108258915,0.00050929945,0.00006370417,0.0006564061,0.000047198446,5.0222025e-7],"category_scores_gemma":[0.00012748128,0.00012583593,0.00002982617,0.0004655484,0.00017690157,0.0009608152,0.00036687538,0.00012690625,0.0000026645153],"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":[0.0000086188775,0.00016944972,0.0006209577,0.000013350291,0.000008396717,2.7009332e-7,0.00046571353,0.022877166,0.00014101606,0.95324516,0.00014471795,0.022305159],"study_design_scores_gemma":[0.00029688276,0.000013015096,0.004146889,0.000011942504,0.0000015770016,0.00001223716,0.000003660392,0.55792975,0.000040449417,0.4373296,0.00013446224,0.00007952947],"about_ca_topic_score_codex":0.0000042116094,"about_ca_topic_score_gemma":0.0000029895757,"teacher_disagreement_score":0.5350526,"about_ca_system_score_codex":0.000078625904,"about_ca_system_score_gemma":0.00014604497,"threshold_uncertainty_score":0.513144},"labels":[],"label_agreement":null},{"id":"W2912536233","doi":"10.1007/s11263-006-0001-4","title":"Video Epitomes","year":2006,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Epitome; Computer science; Artificial intelligence; Inference; Interpolation (computer graphics); Computer vision; Representation (politics); Pattern recognition (psychology); Image (mathematics)","score_opus":0.005584354788841344,"score_gpt":0.30031664946610187,"score_spread":0.29473229467726053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912536233","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042262105,0.00022614762,0.9909348,0.0025431875,0.0015988399,0.00002651208,4.802681e-7,0.0000995102,0.00034431423],"genre_scores_gemma":[0.3527275,0.00001568145,0.64621246,0.00028702238,0.00071305485,4.0666418e-7,7.890821e-7,0.0000053007384,0.00003779442],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986079,0.00003462139,0.00044635514,0.00014660205,0.00064755813,0.00011699198],"domain_scores_gemma":[0.9984855,0.0000882053,0.00039585144,0.0001702248,0.0008184211,0.000041802152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027080957,0.000099506935,0.00013792585,0.00031371537,0.000036032532,0.00032688447,0.0016940417,0.000033826393,0.000005389803],"category_scores_gemma":[0.000021011172,0.00008292937,0.00010161023,0.0001363956,0.00003268439,0.0016520678,0.00032585557,0.00014735242,0.000016829676],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059574166,0.00036244772,0.0010599833,0.000008733305,0.00006110228,0.00073098287,0.0001499792,0.0013704252,0.027984628,0.043690793,0.04623806,0.87828326],"study_design_scores_gemma":[0.0018397679,0.00092699175,0.018591052,0.00064419187,0.000012096718,0.0036535687,0.0000042521237,0.30840653,0.037556197,0.482032,0.14576319,0.0005701747],"about_ca_topic_score_codex":0.0000036053034,"about_ca_topic_score_gemma":3.61143e-7,"teacher_disagreement_score":0.87771314,"about_ca_system_score_codex":0.000077396144,"about_ca_system_score_gemma":0.00005373264,"threshold_uncertainty_score":0.33817616},"labels":[],"label_agreement":null},{"id":"W2943486172","doi":"10.1109/iscas.2019.8702351","title":"SRSubBandNet: A New Deep Learning Scheme for Single Image Super Resolution Based on Subband Reconstruction","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Benchmark (surveying); Computer science; Residual; Scheme (mathematics); Artificial intelligence; Convolutional neural network; Superresolution; Image (mathematics); Deep learning; Iterative reconstruction; Low resolution; Pattern recognition (psychology); Computer vision; High resolution; Algorithm; Mathematics","score_opus":0.01383057780873389,"score_gpt":0.2497406450390063,"score_spread":0.2359100672302724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943486172","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028704386,0.00006426426,0.98772144,0.00054452446,0.00019687282,0.0003534055,3.7641948e-7,0.00088341977,0.0073652556],"genre_scores_gemma":[0.07889506,0.000004012297,0.91942865,0.00021958102,0.000068054615,0.00002208638,0.0000050402577,0.000020181278,0.0013373083],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987749,0.000035490637,0.00019798592,0.000502014,0.0001873476,0.00030226383],"domain_scores_gemma":[0.9991566,0.00012010449,0.00009940828,0.00040065555,0.00014623867,0.00007700617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019561265,0.00015753429,0.00015664443,0.00015703613,0.00014303719,0.00022503048,0.00031508936,0.00008177311,0.00005892974],"category_scores_gemma":[0.00015332241,0.00014978118,0.000067584435,0.0002680595,0.00003704029,0.0012013838,0.000048788792,0.00013404888,0.000052027284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001622524,0.00014525262,0.0015085858,0.000078798985,0.000008740224,0.0000026231937,0.00022365792,0.0005372953,0.6267938,0.004612352,0.001109731,0.36481693],"study_design_scores_gemma":[0.0008313312,0.0006140649,0.00007323926,0.00006942649,0.0000032439898,0.000017748747,0.0000143094685,0.8659253,0.11936295,0.007886458,0.0049457946,0.00025609988],"about_ca_topic_score_codex":0.0000100119105,"about_ca_topic_score_gemma":0.000003361701,"teacher_disagreement_score":0.86538804,"about_ca_system_score_codex":0.00011789078,"about_ca_system_score_gemma":0.00007067583,"threshold_uncertainty_score":0.6107899},"labels":[],"label_agreement":null},{"id":"W2950226402","doi":"10.1007/978-3-030-33843-5_22","title":"Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy","year":2019,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Pooling; Computer science; Artificial intelligence; Usability; Image quality; Channel (broadcasting); Miniaturization; Bilinear interpolation; Computer vision; Flexibility (engineering); Image resolution; Image (mathematics); Mathematics; Statistics; Engineering; Telecommunications","score_opus":0.010846761873251337,"score_gpt":0.2988410004496852,"score_spread":0.28799423857643386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950226402","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001629175,0.00020546427,0.9925053,0.0020683133,0.0014424877,0.0013513973,0.0000060977122,0.0007721417,0.000019628107],"genre_scores_gemma":[0.31977376,0.00000919717,0.6785387,0.0013508532,0.00021077797,0.000084016174,0.0000072866123,0.000024443298,9.675786e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948457,0.00011316634,0.0006621617,0.0026072306,0.0008599025,0.0009118538],"domain_scores_gemma":[0.99582565,0.00031541483,0.00030242454,0.0027461876,0.0005945143,0.00021578855],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.001418314,0.00055212015,0.00057626615,0.0009052666,0.00024304586,0.00111424,0.0056402357,0.00034374755,0.0000022708239],"category_scores_gemma":[0.00039851523,0.0005419531,0.00011293541,0.0020042288,0.00054274156,0.0010883277,0.0064737303,0.001208003,0.00009211137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003444437,0.00012794,0.00026833723,0.00017735938,0.0000065730014,0.000017540802,0.0013941311,0.19794156,0.3317495,0.00028186958,0.000049137514,0.4679516],"study_design_scores_gemma":[0.00019304435,0.00008424543,0.00012735355,0.00018583762,0.0000033293375,0.000027418766,1.8535891e-7,0.82786566,0.1346503,0.03621556,0.00014154673,0.0005055094],"about_ca_topic_score_codex":0.00011347908,"about_ca_topic_score_gemma":0.0000167093,"teacher_disagreement_score":0.6299241,"about_ca_system_score_codex":0.00064329436,"about_ca_system_score_gemma":0.0008773321,"threshold_uncertainty_score":0.9999227},"labels":[],"label_agreement":null},{"id":"W2950579961","doi":"10.48550/arxiv.1806.08338","title":"Can Deep Learning Relax Endomicroscopy Hardware Miniaturization Requirements?","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Miniaturization; Computer science; Endomicroscopy; Artificial intelligence; Software; Deep learning; Convolutional neural network; Confocal; Computer vision; Computer hardware; Materials science; Optics; Physics; Nanotechnology","score_opus":0.05081416365286252,"score_gpt":0.22054668306734534,"score_spread":0.1697325194144828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950579961","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012677989,0.00012033063,0.98335195,0.00012488174,0.00053644413,0.00032076263,0.0000063255416,0.0013514619,0.0015098522],"genre_scores_gemma":[0.75024515,0.00024635793,0.247177,0.000116563824,0.00010449201,0.000002859893,0.000043783126,0.0000417263,0.0020220343],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974231,0.00019088687,0.00027704128,0.0014957328,0.00013925754,0.0004739548],"domain_scores_gemma":[0.99750847,0.000062535066,0.0005875562,0.0012795114,0.00041167147,0.00015023726],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000278821,0.0004093415,0.000356833,0.00033134135,0.00038774929,0.00029562743,0.0023461434,0.0003462951,0.000023813627],"category_scores_gemma":[0.0002059219,0.00051011523,0.00014549542,0.00061780587,0.00024712158,0.000875572,0.0030814258,0.0008378306,0.0000578151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00095732225,0.001578573,0.058720205,0.0029150406,0.0016108028,0.0045289127,0.016510809,0.19625726,0.06709527,0.5868147,0.010820932,0.052190155],"study_design_scores_gemma":[0.00111566,0.0002244563,0.00036616065,0.0006986059,0.0001228522,0.000016227295,0.00009297759,0.73339903,0.020787343,0.23678018,0.0048105503,0.0015859723],"about_ca_topic_score_codex":0.00003942666,"about_ca_topic_score_gemma":0.000017987051,"teacher_disagreement_score":0.7375672,"about_ca_system_score_codex":0.0005160352,"about_ca_system_score_gemma":0.00022900202,"threshold_uncertainty_score":0.99973506},"labels":[],"label_agreement":null},{"id":"W2951720195","doi":"","title":"Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Normalization (sociology); Computer science; Artificial intelligence; Convolutional neural network; Deep learning; Machine learning; Artificial neural network; Pattern recognition (psychology)","score_opus":0.0711780634921327,"score_gpt":0.20990306906674397,"score_spread":0.13872500557461126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951720195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00910972,0.0009172924,0.98620945,0.00016296202,0.000322654,0.0002600573,0.0000019453557,0.0007729694,0.0022429381],"genre_scores_gemma":[0.9349767,0.0007348023,0.0637657,0.00014423927,0.000155245,0.0000028809923,0.0000047335534,0.000026721827,0.00018898938],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979944,0.0001460078,0.00026226483,0.00094820757,0.0001202166,0.0005288788],"domain_scores_gemma":[0.9980212,0.00015038664,0.00044647238,0.0010838049,0.00018161458,0.00011650738],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048810785,0.00036632552,0.000331077,0.00016965525,0.0006324564,0.00041075094,0.0019703067,0.00018830293,0.000004863275],"category_scores_gemma":[0.000057041652,0.00030601578,0.000099820216,0.00054076914,0.00024718698,0.0016406806,0.0045955353,0.0005002741,0.000009500548],"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":[0.00004134582,0.000043026455,0.03787842,0.00029206625,0.00013278946,0.0001601122,0.00060372223,0.04828692,0.00034755672,0.90313095,0.0008871871,0.008195906],"study_design_scores_gemma":[0.00028793688,0.00002055161,0.0007588149,0.0006340045,0.00005055004,0.000023316266,0.00003602264,0.82817614,0.00047668116,0.1662519,0.002699448,0.0005846499],"about_ca_topic_score_codex":0.000033718876,"about_ca_topic_score_gemma":0.0000115942385,"teacher_disagreement_score":0.92586696,"about_ca_system_score_codex":0.00016021781,"about_ca_system_score_gemma":0.000094997886,"threshold_uncertainty_score":0.9999392},"labels":[],"label_agreement":null},{"id":"W2962360676","doi":"10.1145/3306346.3322996","title":"Hyperparameter optimization in black-box image processing using differentiable proxies","year":2019,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; McGill University","funders":"","keywords":"Computer science; Black box; Software; Pipeline (software); Artificial intelligence; Convolutional neural network; Image processing; Computer engineering; Computer hardware; Real-time computing; Image (mathematics)","score_opus":0.01982256747076111,"score_gpt":0.27517203226417114,"score_spread":0.25534946479341003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962360676","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03240636,0.00005379205,0.9662782,0.00028889213,0.000081736674,0.00033676848,0.0000018698083,0.00044854297,0.000103841914],"genre_scores_gemma":[0.39842427,0.000035682915,0.6013192,0.00012983309,0.0000048388565,0.000020996957,0.000001301513,0.000019901914,0.00004399315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985469,0.000052677402,0.00028648306,0.0005091703,0.00027672065,0.00032805375],"domain_scores_gemma":[0.9988122,0.0000733513,0.00011429427,0.00080424844,0.00014596886,0.000049937727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015816833,0.00021441064,0.00021021708,0.0005519541,0.0001677506,0.00029143362,0.0008017256,0.00011266052,0.000017550932],"category_scores_gemma":[0.000032478405,0.00021331293,0.00007095048,0.0013146577,0.00011539768,0.0020960905,0.000029725592,0.00037681486,0.000009532717],"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":[0.00024554675,0.0036840646,0.009179649,0.001854173,0.00015165196,0.000057857516,0.0072339694,0.60471565,0.18112524,0.0051885,0.000068562775,0.18649517],"study_design_scores_gemma":[0.00032484147,0.00007487624,0.00018033796,0.00020796369,0.000012470665,0.000011899277,0.000040082527,0.9632347,0.021736622,0.013838672,0.000030395802,0.0003071146],"about_ca_topic_score_codex":0.000014841382,"about_ca_topic_score_gemma":0.000005430686,"teacher_disagreement_score":0.3660179,"about_ca_system_score_codex":0.00007134133,"about_ca_system_score_gemma":0.000078652345,"threshold_uncertainty_score":0.8698649},"labels":[],"label_agreement":null},{"id":"W2963420948","doi":"10.1109/tip.2018.2847421","title":"Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Benchmark (surveying); Deep learning; Computer vision; Semantics (computer science); Segmentation; Pattern recognition (psychology)","score_opus":0.024271875803380263,"score_gpt":0.26880956850429366,"score_spread":0.2445376927009134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963420948","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005246103,0.00034481147,0.9953185,0.0007475037,0.0005763187,0.00024135553,0.000004154873,0.0020254713,0.00021731322],"genre_scores_gemma":[0.5047722,0.00001622721,0.49437457,0.00051077927,0.00013362156,0.000048677248,0.0000018770692,0.0000533033,0.00008876722],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968991,0.00008897845,0.0006023394,0.0009982378,0.0005069482,0.0009043587],"domain_scores_gemma":[0.9980497,0.00011649434,0.00033769966,0.0007259748,0.0005615061,0.00020866406],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00032215304,0.00049203617,0.00037202408,0.0003271789,0.0013607264,0.0010487562,0.0012930145,0.00017979716,0.000022993989],"category_scores_gemma":[0.000033524193,0.0005159736,0.00011674015,0.0012937823,0.00040763585,0.0036127663,0.000024997515,0.00073815323,0.00003375136],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025696685,0.0002548271,0.000007963754,0.00013673992,0.000027767992,0.00003185293,0.0014807305,0.0032126342,0.045380335,0.000051852687,0.00022235457,0.94916725],"study_design_scores_gemma":[0.00033456567,0.000114212664,0.000008427112,0.00016069935,0.000022119031,0.00007627697,0.0001265058,0.89424706,0.10267968,0.0015660129,0.00011842734,0.00054603623],"about_ca_topic_score_codex":0.000014271304,"about_ca_topic_score_gemma":0.000011315722,"teacher_disagreement_score":0.9486212,"about_ca_system_score_codex":0.0001440618,"about_ca_system_score_gemma":0.00007219754,"threshold_uncertainty_score":0.99998826},"labels":[],"label_agreement":null},{"id":"W2963610452","doi":"10.1109/cvpr.2019.00399","title":"Feedback Network for Image Super-Resolution","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":864,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Code (set theory); Image (mathematics); Block (permutation group theory); Deep learning; Recurrent neural network; Artificial neural network; Iterative reconstruction; Computer vision; Machine learning; Pattern recognition (psychology)","score_opus":0.010673608494475791,"score_gpt":0.26564340424377286,"score_spread":0.25496979574929707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963610452","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030844903,0.00008374197,0.98987436,0.00086947234,0.00020138705,0.0002960016,6.9996463e-7,0.00085502403,0.007510845],"genre_scores_gemma":[0.015369824,0.00000416636,0.9810527,0.00056874467,0.00007792628,0.000032271386,0.0000023840244,0.000010349664,0.0028816604],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991742,0.000011985757,0.00012206657,0.00029863318,0.000104434504,0.00028872737],"domain_scores_gemma":[0.9993121,0.00006156748,0.000040202984,0.00043642108,0.00011263255,0.00003705045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018083377,0.000090350804,0.00010043719,0.000029614372,0.00007457714,0.00013144441,0.00058551814,0.000038284,0.000026063204],"category_scores_gemma":[0.000029404422,0.000080248305,0.00004334025,0.00019598729,0.000023568755,0.0010556348,0.0001853821,0.00005838655,0.00014751598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041502204,0.00012153481,0.0011404391,0.00016371025,0.00002057203,0.0000046709483,0.0002580373,0.00043316267,0.096770786,0.5100237,0.2484438,0.1425781],"study_design_scores_gemma":[0.00040259943,0.00016150283,0.00032799813,0.000040081333,0.0000030368037,0.0000127016165,0.000007884447,0.7251478,0.017200334,0.19877374,0.05759007,0.00033226752],"about_ca_topic_score_codex":0.0000042320435,"about_ca_topic_score_gemma":0.000001486423,"teacher_disagreement_score":0.72471464,"about_ca_system_score_codex":0.000031725678,"about_ca_system_score_gemma":0.00003057144,"threshold_uncertainty_score":0.3272431},"labels":[],"label_agreement":null},{"id":"W2967576448","doi":"10.1007/978-3-030-27202-9_11","title":"Locally Linear Image Structural Embedding for Image Structure Manifold Learning","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Embedding; Nonlinear dimensionality reduction; Manifold (fluid mechanics); Image (mathematics); Kernel (algebra); Computer science; Artificial intelligence; Norm (philosophy); Manifold alignment; Mathematics; Pattern recognition (psychology); Discrete mathematics; Dimensionality reduction","score_opus":0.010747471121568427,"score_gpt":0.2846919609537916,"score_spread":0.2739444898322232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967576448","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000052583902,0.0003302485,0.9953024,0.00036688425,0.0013603162,0.0009573597,0.000021054642,0.0008180642,0.0007911202],"genre_scores_gemma":[0.020902788,0.000017099128,0.9769502,0.00073860015,0.00058417907,0.000011831462,0.000020841611,0.00011669363,0.00065774086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9943753,0.000041123258,0.0007619768,0.0025401667,0.0010893304,0.0011921177],"domain_scores_gemma":[0.99605453,0.0005929412,0.00068242365,0.0016628669,0.00080903753,0.00019818758],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000684232,0.0009793137,0.00088802946,0.000910406,0.00055857643,0.0013097661,0.0051536197,0.00051742257,0.000028485365],"category_scores_gemma":[0.00037799386,0.0009106466,0.00023847481,0.00057985063,0.0006844369,0.002427629,0.0023171909,0.001877363,0.0000261661],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051370753,0.000022369277,0.000050950945,0.0007785638,0.00004648964,0.00026335465,0.0014348415,0.05080996,0.04240722,0.0320903,0.00009970522,0.87194484],"study_design_scores_gemma":[0.00030129863,0.00022517341,0.000010670732,0.00048415636,0.000010927505,0.00014578304,3.0206286e-7,0.7831077,0.011152237,0.20276655,0.0008710353,0.00092414225],"about_ca_topic_score_codex":0.0000065870586,"about_ca_topic_score_gemma":0.000013470101,"teacher_disagreement_score":0.87102073,"about_ca_system_score_codex":0.0004833309,"about_ca_system_score_gemma":0.000872453,"threshold_uncertainty_score":0.99972695},"labels":[],"label_agreement":null},{"id":"W2969772853","doi":"10.1016/j.imavis.2019.08.008","title":"Self-supervised blur detection from synthetically blurred scenes","year":2019,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España; Eusko Jaurlaritza","keywords":"Artificial intelligence; Computer science; Ground truth; Computer vision; Segmentation; Image (mathematics); Pattern recognition (psychology); Image restoration; Object (grammar); Image segmentation; Image processing","score_opus":0.006246579469884188,"score_gpt":0.2789864774374947,"score_spread":0.27273989796761056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969772853","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11824761,0.00023022271,0.8793847,0.00024000343,0.00020837918,0.00015740162,7.856536e-7,0.0010618902,0.00046900255],"genre_scores_gemma":[0.487933,0.00001293856,0.5118585,0.0001290653,0.000045161876,0.0000011502958,7.4376396e-7,0.000010190211,0.000009207281],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985517,0.00007383244,0.0002607459,0.0006079453,0.00023173643,0.00027403168],"domain_scores_gemma":[0.9989534,0.00023787815,0.00010766476,0.0004701281,0.00014358912,0.000087331784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026771004,0.00018159903,0.0002092172,0.00010078804,0.00020709106,0.00049955095,0.0004975504,0.000070538284,0.00000997354],"category_scores_gemma":[0.00007517381,0.00016829469,0.00004804074,0.00024915166,0.00004195263,0.0009854414,0.00060731976,0.00018317826,0.00007277612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008495129,0.00005542552,0.0002808112,0.000047052614,0.000008445685,0.000010385543,0.0003779153,0.0000057801017,0.24937004,0.000218552,0.00003083379,0.7495863],"study_design_scores_gemma":[0.00030766142,0.00010282677,0.0007474941,0.0001493122,0.0000060805837,0.000017849561,0.00002154849,0.9342751,0.05938137,0.0041469773,0.00060890627,0.00023486196],"about_ca_topic_score_codex":0.000013846562,"about_ca_topic_score_gemma":7.1986295e-7,"teacher_disagreement_score":0.9342693,"about_ca_system_score_codex":0.000026182957,"about_ca_system_score_gemma":0.000025332356,"threshold_uncertainty_score":0.6862858},"labels":[],"label_agreement":null},{"id":"W2970009752","doi":"10.1109/icip.2019.8803374","title":"Deep Jpeg Image Deblocking Using Residual Maxout Units","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Deblocking filter; Computer science; Artificial intelligence; JPEG; Lossy compression; Computer vision; Image compression; Residual; Image restoration; Image (mathematics); Transform coding; Compression artifact; Image processing; Discrete cosine transform; Algorithm","score_opus":0.023259351936172196,"score_gpt":0.28445109146340475,"score_spread":0.26119173952723257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970009752","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011722489,0.00010628033,0.9770057,0.00018382714,0.00012688608,0.00013733,2.2871086e-7,0.0010058495,0.009711381],"genre_scores_gemma":[0.15089853,0.000002980989,0.84800714,0.00039442326,0.00003292439,0.000002876713,6.676484e-7,0.000017587125,0.0006428918],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871224,0.00003892697,0.00019951328,0.00043076184,0.0002610173,0.00035756282],"domain_scores_gemma":[0.99886626,0.00006258753,0.000093955496,0.0006749961,0.000236555,0.00006565813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020319564,0.00015677519,0.00015209084,0.00013566871,0.00011874139,0.00028675087,0.00097822,0.000055920194,0.000042087562],"category_scores_gemma":[0.00009146025,0.00014495081,0.000023870956,0.0006295574,0.000043709235,0.0016871585,0.0006119078,0.00016293535,0.00009533694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026415086,0.00014171933,0.005852639,0.00021738127,0.000047365178,0.00026119547,0.0023500843,0.0007692986,0.7455165,0.068084426,0.0019470879,0.1747859],"study_design_scores_gemma":[0.00023130781,0.000045633486,0.00010954445,0.00007828667,0.0000047900653,0.000108628585,0.00005593328,0.8657522,0.113918215,0.018261798,0.001039854,0.00039378507],"about_ca_topic_score_codex":0.000023368922,"about_ca_topic_score_gemma":0.0000039090605,"teacher_disagreement_score":0.8649829,"about_ca_system_score_codex":0.00006612034,"about_ca_system_score_gemma":0.000102333266,"threshold_uncertainty_score":0.5910923},"labels":[],"label_agreement":null},{"id":"W2970140064","doi":"10.1109/icip.2019.8803300","title":"Single Image Super-Resolution via Cascaded Parallel Multisize Receptive Field","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Kernel (algebra); Computer science; Convolution (computer science); Image (mathematics); Artificial intelligence; Convolutional neural network; Image resolution; Computer vision; Field (mathematics); Superresolution; Pattern recognition (psychology); Receptive field; Resolution (logic); Artificial neural network; Mathematics","score_opus":0.012195420594649049,"score_gpt":0.2608504922938918,"score_spread":0.24865507169924275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970140064","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018831405,0.00006940816,0.98350084,0.0016913272,0.00017958868,0.00026275692,5.7304004e-7,0.0010476395,0.011364709],"genre_scores_gemma":[0.17197077,0.000007851272,0.8247894,0.000824168,0.000027410573,0.000018181036,0.000001673192,0.000012190611,0.0023483282],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99868536,0.000046783684,0.00021129391,0.00049871404,0.00022176815,0.00033607343],"domain_scores_gemma":[0.9989411,0.00014517756,0.00007747085,0.0006159612,0.00015072766,0.00006956545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014408381,0.00016698614,0.00017095983,0.00008200766,0.00008495498,0.0001460664,0.000746264,0.00009837669,0.00015594214],"category_scores_gemma":[0.000117063355,0.00015091817,0.000059219758,0.0002699039,0.000043861735,0.0016793186,0.0003536787,0.00019124012,0.00039824788],"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":[0.000032172735,0.00018263819,0.00026182612,0.000031169187,0.000012878243,0.000018154662,0.00079437543,0.000022958011,0.82373834,0.005176928,0.0059577776,0.16377078],"study_design_scores_gemma":[0.0009118608,0.0006716143,0.00036694267,0.000086619766,0.000006793846,0.00007106407,0.00007879163,0.4113678,0.5473712,0.028815523,0.009445852,0.00080594013],"about_ca_topic_score_codex":0.00008402463,"about_ca_topic_score_gemma":0.000011223142,"teacher_disagreement_score":0.41134486,"about_ca_system_score_codex":0.00008264648,"about_ca_system_score_gemma":0.00002848492,"threshold_uncertainty_score":0.6154265},"labels":[],"label_agreement":null},{"id":"W2970919733","doi":"10.1109/icip.2019.8803201","title":"Joint Demosaicking and Blind Deblurring Using Deep Convolutional Neural Network","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Deblurring; Demosaicing; Artificial intelligence; Computer science; Computer vision; Convolutional neural network; RGB color model; Image restoration; Pattern recognition (psychology); Image processing; Image (mathematics); Color image","score_opus":0.03106958864415913,"score_gpt":0.2742562938103126,"score_spread":0.24318670516615346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970919733","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06918778,0.0004627186,0.9290594,0.00017872138,0.0001504434,0.00011188541,8.6587065e-8,0.00037631116,0.0004726962],"genre_scores_gemma":[0.43005028,0.0000027495312,0.5694791,0.0003882559,0.00004336564,0.0000016395699,2.6532123e-7,0.0000063798752,0.00002794639],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989696,0.000025560586,0.00017929757,0.00035126926,0.00016312324,0.0003111305],"domain_scores_gemma":[0.99949235,0.00004070083,0.0000816831,0.00025147074,0.000069848385,0.000063926374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019746342,0.00012114503,0.00013588053,0.000058119957,0.00015641368,0.00018873654,0.00029292455,0.000042327654,0.000013270361],"category_scores_gemma":[0.000020177758,0.00011375316,0.00002621131,0.00022289639,0.00004731866,0.0010375947,0.0004912987,0.00013770319,0.000007814594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005549551,0.00014553287,0.123401165,0.00029724123,0.00010187994,0.00008755626,0.0012551714,0.08610207,0.16187261,0.2760029,0.00076123094,0.34991714],"study_design_scores_gemma":[0.00018430983,0.000018284016,0.0011423448,0.00004005363,0.0000023945322,0.000083941406,0.0000050374215,0.98118395,0.0012042571,0.015857264,0.00011903943,0.00015912447],"about_ca_topic_score_codex":0.000012752131,"about_ca_topic_score_gemma":0.0000022635481,"teacher_disagreement_score":0.8950819,"about_ca_system_score_codex":0.000045228164,"about_ca_system_score_gemma":0.00003642235,"threshold_uncertainty_score":0.46387193},"labels":[],"label_agreement":null},{"id":"W2971262104","doi":"10.1109/icip.2019.8803167","title":"UPDCNN: A New Scheme for Image Upsampling and Deblurring Using a Deep Convolutional Neural Network","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Deblurring; Upsampling; Computer science; Convolutional neural network; Artificial intelligence; Scheme (mathematics); Computer vision; Image (mathematics); Deep learning; Image restoration; Pattern recognition (psychology); Image processing; Mathematics","score_opus":0.025020058982218304,"score_gpt":0.29782204083086466,"score_spread":0.27280198184864635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971262104","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009696856,0.00048411224,0.9885294,0.00024177872,0.00014378381,0.00027546394,3.7100557e-7,0.00045254416,0.00017572148],"genre_scores_gemma":[0.03733491,0.0000030154285,0.9619177,0.0004518729,0.00013901829,0.000009233912,0.0000010419011,0.000015784402,0.00012744161],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887943,0.000010858535,0.00018395518,0.00041629077,0.00012816477,0.0003813219],"domain_scores_gemma":[0.9993644,0.00009144752,0.00008493828,0.000262406,0.00010098507,0.00009582326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016969183,0.00014359472,0.0001590402,0.000054805856,0.00016387168,0.0002429774,0.00037148848,0.00004478245,0.000009843448],"category_scores_gemma":[0.000051826402,0.00014028125,0.000041890322,0.00019815036,0.000038494607,0.0012604553,0.00037828495,0.00010329472,0.00000419058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013339431,0.00013021029,0.020640548,0.0006281104,0.00012664846,0.000027917282,0.001244013,0.039175157,0.31543434,0.470694,0.0030290005,0.14873664],"study_design_scores_gemma":[0.0002833351,0.000030107287,0.00010235346,0.00004400468,0.0000030966617,0.000049568036,0.00000749485,0.95776564,0.00090294733,0.040267546,0.00036029497,0.0001836317],"about_ca_topic_score_codex":0.000015995489,"about_ca_topic_score_gemma":0.0000023677183,"teacher_disagreement_score":0.9185905,"about_ca_system_score_codex":0.00004373654,"about_ca_system_score_gemma":0.00008162752,"threshold_uncertainty_score":0.57205033},"labels":[],"label_agreement":null},{"id":"W2972324374","doi":"10.1109/iccvw.2019.00409","title":"Edge-Informed Single Image Super-Resolution","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Inpainting; Artificial intelligence; Computer science; Computer vision; Interpolation (computer graphics); Image (mathematics); Image texture; Code (set theory); Pyramid (geometry); Pattern recognition (psychology); Image processing; Mathematics","score_opus":0.027878050759110914,"score_gpt":0.30172936626113755,"score_spread":0.2738513155020266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972324374","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011778517,0.00025033386,0.93767846,0.0009685262,0.00085885444,0.00044344141,0.000004673594,0.0027938853,0.056884073],"genre_scores_gemma":[0.020038513,0.000043074528,0.975328,0.00047524725,0.00010782617,0.000061464554,0.000026688322,0.00003258306,0.003886554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977586,0.00003905723,0.0004205543,0.00087930437,0.0004163132,0.00048614288],"domain_scores_gemma":[0.99731505,0.00008761223,0.00024402529,0.0019116696,0.00034455545,0.000097085554],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023139012,0.00038095863,0.00036801145,0.0002443004,0.00009701165,0.0007357254,0.0025613199,0.0003134337,0.000044928864],"category_scores_gemma":[0.00022011642,0.00035837467,0.00014749375,0.00023494077,0.00010409672,0.0016056055,0.004675003,0.0006374484,0.00034074206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006135444,0.0012011473,0.00030230585,0.0039181286,0.0001750706,0.00012231988,0.0036488764,0.0012685527,0.1848527,0.07454359,0.27755132,0.45235464],"study_design_scores_gemma":[0.0004616655,0.00021094066,0.00016336319,0.00074833323,0.000023631244,0.000054970526,0.000032067783,0.64498824,0.13580883,0.17497523,0.040659398,0.001873333],"about_ca_topic_score_codex":0.000034961875,"about_ca_topic_score_gemma":0.0000066793186,"teacher_disagreement_score":0.6437197,"about_ca_system_score_codex":0.00036174754,"about_ca_system_score_gemma":0.00050850864,"threshold_uncertainty_score":0.9998868},"labels":[],"label_agreement":null},{"id":"W2973984369","doi":"","title":"LISR: Image Super-resolution under Hardware Constraints.","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Upsampling; Computer science; Lossy compression; Image compression; JPEG; Artificial intelligence; Image (mathematics); Truncation (statistics); Compressed sensing; Computer vision; Computer hardware; Computer engineering; Image processing","score_opus":0.06420014968139266,"score_gpt":0.21388127192303275,"score_spread":0.1496811222416401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973984369","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003936226,0.00012408484,0.9860211,0.00028242305,0.0005390562,0.0003988386,0.000033377855,0.0014438595,0.0072210412],"genre_scores_gemma":[0.8207315,0.00012645852,0.17678784,0.00024354056,0.00005616128,0.0000017914682,0.000030380439,0.00003432149,0.0019880116],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99736524,0.00013435581,0.00024839377,0.0015752882,0.00015019668,0.0005265441],"domain_scores_gemma":[0.99714637,0.00009275857,0.00029471985,0.0019035332,0.0003987691,0.00016386315],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024425783,0.00044999283,0.00041916044,0.0003327037,0.00018719191,0.00030132133,0.0027606918,0.00040199584,0.00006306036],"category_scores_gemma":[0.00006754553,0.0005422441,0.00023321435,0.00051367,0.00047005859,0.0013858157,0.0034617682,0.0009316189,0.00027061545],"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":[0.00009088212,0.00051150954,0.0019781673,0.00094284123,0.00031687733,0.0013565597,0.00078404933,0.12643148,0.00623759,0.8419021,0.010006854,0.009441126],"study_design_scores_gemma":[0.0005413738,0.00006477276,0.00037742357,0.00034523936,0.00006423338,0.000027833657,0.000097949014,0.7386882,0.0018018892,0.25549608,0.0014162912,0.0010787518],"about_ca_topic_score_codex":0.000048609,"about_ca_topic_score_gemma":0.000008807011,"teacher_disagreement_score":0.8167953,"about_ca_system_score_codex":0.00045167914,"about_ca_system_score_gemma":0.00046552293,"threshold_uncertainty_score":0.99970293},"labels":[],"label_agreement":null},{"id":"W2978824489","doi":"10.1109/ijcnn.2019.8852320","title":"Edge focused super-resolution of thermal images","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Agence Nationale de la Recherche","keywords":"Enhanced Data Rates for GSM Evolution; Benchmark (surveying); Computer science; Artificial intelligence; Focus (optics); Computer vision; Residual; Superresolution; Resolution (logic); Low resolution; Image resolution; Edge detection; Feature extraction; Thermal; Image (mathematics); High resolution; Pattern recognition (psychology); Image processing; Remote sensing; Algorithm; Optics; Geography","score_opus":0.011143746691214998,"score_gpt":0.2526228305700068,"score_spread":0.2414790838787918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978824489","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015336802,0.00010726348,0.9669752,0.00033453427,0.00007105837,0.00010257745,5.0702323e-7,0.00043971182,0.016632311],"genre_scores_gemma":[0.46300343,0.0000021481321,0.5361658,0.00006581,0.0000080500095,0.0000031380764,2.7353488e-7,0.000004218867,0.00074718276],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993535,0.000020653879,0.00013092256,0.00020237782,0.00014281314,0.00014969042],"domain_scores_gemma":[0.99935025,0.000033950622,0.00005344247,0.00044335128,0.0000954173,0.000023559527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012294462,0.00007393629,0.00010307871,0.000060478673,0.000024201612,0.000035669495,0.00059886806,0.000030849074,0.000052362924],"category_scores_gemma":[0.000022282282,0.000060520175,0.000033185093,0.00017558268,0.000040617026,0.0008760121,0.00021847573,0.000059749334,0.00005704004],"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":[0.0000054974107,0.000052026426,0.0013482452,0.000030148944,0.000004334573,0.0000013439263,0.0001639073,0.000015521979,0.89149255,0.020866541,0.001165701,0.084854186],"study_design_scores_gemma":[0.0002761175,0.00012543733,0.003019906,0.000031612195,0.0000021223848,0.0000043631553,0.000008860703,0.056651782,0.92688906,0.01147249,0.0013320667,0.00018617963],"about_ca_topic_score_codex":0.00001253171,"about_ca_topic_score_gemma":4.4255256e-7,"teacher_disagreement_score":0.44766662,"about_ca_system_score_codex":0.000015888567,"about_ca_system_score_gemma":0.000033536846,"threshold_uncertainty_score":0.2467941},"labels":[],"label_agreement":null},{"id":"W2979618460","doi":"10.1109/tmm.2019.2946094","title":"Light Field Super-Resolution Using Edge-Preserved Graph-Based Regularization","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Light field; Computer science; Computer vision; Artificial intelligence; Iterative reconstruction; Graph; Image resolution; Regularization (linguistics); Field (mathematics); Algorithm; Mathematics; Theoretical computer science","score_opus":0.019042495396324948,"score_gpt":0.2674966561232707,"score_spread":0.2484541607269458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979618460","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004406594,0.000039717826,0.99263114,0.0007553407,0.00083836773,0.00036787547,0.0000035830997,0.000787969,0.00016939323],"genre_scores_gemma":[0.43678337,0.0000051064494,0.56267977,0.00026334185,0.000026997543,0.000028088694,0.000002470249,0.000020021955,0.0001908215],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985396,0.00008404453,0.00026688748,0.00050696504,0.00029395966,0.00030854574],"domain_scores_gemma":[0.9986872,0.0001593173,0.00009506172,0.0007928385,0.00017064226,0.00009495327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016069735,0.00020649,0.00017991695,0.0003626491,0.00020837106,0.00011613033,0.00061482843,0.0001655129,0.000056995912],"category_scores_gemma":[0.000024411136,0.00021226161,0.00011416663,0.00070377917,0.000032490556,0.0010904608,0.0000051574107,0.0003001451,0.000065406006],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010754551,0.0005234373,0.00015225567,0.00010375247,0.000037745307,0.000011044216,0.00050808757,0.05955519,0.8472916,0.00024440818,0.00037125198,0.091093674],"study_design_scores_gemma":[0.00032003282,0.00007980805,0.000021631198,0.00005911102,0.000008352578,0.000002368202,0.0000039903007,0.59508234,0.40324163,0.0008731969,0.00014652133,0.00016100223],"about_ca_topic_score_codex":0.000036067704,"about_ca_topic_score_gemma":0.0000115310095,"teacher_disagreement_score":0.53552717,"about_ca_system_score_codex":0.000098281496,"about_ca_system_score_gemma":0.00010092616,"threshold_uncertainty_score":0.8655777},"labels":[],"label_agreement":null},{"id":"W2982283130","doi":"10.48550/arxiv.1910.11577","title":"CrevNet: Conditionally Reversible Video Prediction","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Autoencoder; Computer science; Bijection; Property (philosophy); Feature (linguistics); Artificial intelligence; Conditional independence; Feature extraction; Resolution (logic); Pattern recognition (psychology); Algorithm; Theoretical computer science; Machine learning; Deep learning; Mathematics; Discrete mathematics","score_opus":0.052449195999658235,"score_gpt":0.19818836338768878,"score_spread":0.14573916738803055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982283130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003697877,0.00011424761,0.9865713,0.0001631952,0.00062497106,0.00036838296,0.000057859794,0.0012560814,0.00714605],"genre_scores_gemma":[0.87187856,0.0002297283,0.12139671,0.00029555915,0.000100039746,0.0000029298535,0.00008279035,0.000030080804,0.005983582],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790114,0.000093364215,0.00022030635,0.0013036622,0.00014810007,0.00033341395],"domain_scores_gemma":[0.99763876,0.00009291533,0.0003552249,0.001456349,0.0003345623,0.00012217529],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023237524,0.00032294614,0.00031134128,0.00033536463,0.00016713722,0.0001668691,0.0021450787,0.00032588854,0.000052795276],"category_scores_gemma":[0.00005919043,0.00040576205,0.00017311871,0.0005253989,0.00012477527,0.0013284559,0.0022653579,0.0006848131,0.0002187756],"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":[0.00011764144,0.00045015846,0.009549589,0.0010578791,0.00036945392,0.0007808089,0.00046943073,0.27602813,0.0024994453,0.6580075,0.048211906,0.0024580767],"study_design_scores_gemma":[0.00040878903,0.00008069821,0.0010133,0.0003348741,0.000054508553,0.000014768198,0.000015447835,0.62688375,0.0008176627,0.36525276,0.0045553404,0.00056810415],"about_ca_topic_score_codex":0.00002439633,"about_ca_topic_score_gemma":0.0000031383684,"teacher_disagreement_score":0.8681807,"about_ca_system_score_codex":0.00037531072,"about_ca_system_score_gemma":0.00038504376,"threshold_uncertainty_score":0.9998394},"labels":[],"label_agreement":null},{"id":"W2982470081","doi":"10.1109/cvprw.2019.00073","title":"RUNet: A Robust UNet Architecture for Image Super-Resolution","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Christie (Canada); University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Image resolution; Image (mathematics); Computer vision; Resolution (logic); Superresolution; Set (abstract data type); Projector; Low resolution; Iterative reconstruction; Sub-pixel resolution; Pattern recognition (psychology); High resolution; Image processing; Digital image processing; Remote sensing","score_opus":0.011583028492485232,"score_gpt":0.2546265459964526,"score_spread":0.24304351750396738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982470081","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004925201,0.000077957244,0.992223,0.0018613414,0.00011254915,0.0004155472,0.0000029375544,0.00095686025,0.003857282],"genre_scores_gemma":[0.014058057,0.000002684062,0.9836937,0.00056276354,0.000039636474,0.000048407102,0.0000051508027,0.000015223649,0.0015743566],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898946,0.000017319724,0.00014259607,0.0004042065,0.00014716537,0.00029922812],"domain_scores_gemma":[0.99918866,0.00006525006,0.000048907463,0.00054171885,0.000106197804,0.000049259626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015181028,0.0001325538,0.00012960962,0.0000894036,0.00007501651,0.00015011868,0.00073254894,0.00005505504,0.000026670188],"category_scores_gemma":[0.00004764022,0.00011149302,0.00005686139,0.00022131414,0.00003751435,0.0006958876,0.00021158051,0.00011216453,0.00005625387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093504714,0.00029565347,0.00046736666,0.00046026957,0.000041511914,0.00001647265,0.001478155,0.0034783431,0.44254753,0.1620099,0.059036598,0.3300747],"study_design_scores_gemma":[0.0006209297,0.00026214554,0.000133708,0.00005246227,0.0000050123786,0.000044696226,0.00001811164,0.79850256,0.036597382,0.103538565,0.059759416,0.00046498433],"about_ca_topic_score_codex":0.000010935791,"about_ca_topic_score_gemma":0.0000065069953,"teacher_disagreement_score":0.7950242,"about_ca_system_score_codex":0.000036076155,"about_ca_system_score_gemma":0.000045303394,"threshold_uncertainty_score":0.45465535},"labels":[],"label_agreement":null},{"id":"W2996680032","doi":"","title":"Efficient and Information-Preserving Future Frame Prediction and Beyond","year":2020,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; MNIST database; Autoencoder; Bottleneck; Artificial intelligence; Frame (networking); Machine learning; Feature extraction; Information bottleneck method; Margin (machine learning); Feature (linguistics); High memory; Key (lock); Deep learning; State (computer science); Algorithm; Mutual information","score_opus":0.019646100709941154,"score_gpt":0.2979189959929102,"score_spread":0.27827289528296906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996680032","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01354594,0.000048243117,0.9298532,0.037556317,0.00020608988,0.00016063894,0.0000115464945,0.0005138617,0.018104156],"genre_scores_gemma":[0.90797025,0.000079613405,0.09063323,0.0010821375,0.00010372859,0.000029397519,0.000022176073,0.0000057574844,0.00007371716],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990683,0.000042608146,0.00019818466,0.0002750703,0.0003090713,0.00010671889],"domain_scores_gemma":[0.9992748,0.00008797596,0.00012863577,0.00013964067,0.00027389394,0.00009505299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000092126946,0.00010200819,0.000081153805,0.00012583299,0.00020452825,0.0005023628,0.000357259,0.00004511972,0.000036055506],"category_scores_gemma":[0.00046979912,0.00010385626,0.00001734977,0.00018105627,0.000058505946,0.0013231619,0.00031422274,0.0003022097,0.0000150895385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056845478,0.00006221163,0.012422748,0.000059080015,0.00006503222,0.0000103073835,0.02317927,0.010761343,0.0069349697,0.8365655,0.001467044,0.10841564],"study_design_scores_gemma":[0.00021316193,0.00007154164,0.006006434,0.000027495775,0.0000033405054,0.000011623014,0.00072915707,0.9822553,0.0001863676,0.00593637,0.004452943,0.00010630447],"about_ca_topic_score_codex":0.000007047091,"about_ca_topic_score_gemma":4.1275484e-7,"teacher_disagreement_score":0.9714939,"about_ca_system_score_codex":0.000021038011,"about_ca_system_score_gemma":0.000038937986,"threshold_uncertainty_score":0.48442957},"labels":[],"label_agreement":null},{"id":"W2999042116","doi":"10.1007/s11042-019-08500-5","title":"Super resolution of single depth image based on multi-dictionary learning with edge feature regularization","year":2020,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Regularization (linguistics); Sparse approximation; Computer vision; Feature (linguistics); Constraint (computer-aided design); Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology); Superresolution; Image (mathematics); Focus (optics); Mathematics; Optics","score_opus":0.024986688820234096,"score_gpt":0.2548652574130843,"score_spread":0.22987856859285022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999042116","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016567187,0.00007638185,0.9965106,0.0020984176,0.0000072346784,0.0003621084,0.000010265879,0.00034946465,0.0004198076],"genre_scores_gemma":[0.13781077,0.000009969661,0.8615915,0.00024198962,0.000043993172,0.00015517136,0.00007240797,0.000013394203,0.00006076484],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992066,0.000031936146,0.00013351005,0.00034791825,0.00015939624,0.000120610224],"domain_scores_gemma":[0.99932885,0.0001015974,0.00010928943,0.00023027183,0.00015225909,0.00007770907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006359185,0.000113264374,0.00011371427,0.000055432156,0.00017828058,0.000076667886,0.0002070227,0.000059717375,0.0000023514058],"category_scores_gemma":[0.000106516,0.00010075196,0.000021080565,0.00041415376,0.00010207407,0.00047680616,0.00006110675,0.0001658574,0.000004185217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000092056434,0.0006043557,0.0025790415,0.00017990243,0.000019208248,0.000004428235,0.0012740871,0.0051397826,0.64265245,0.0036003022,0.00090085075,0.3429535],"study_design_scores_gemma":[0.00044615395,0.00017286027,0.0029078368,0.000044725293,0.0000091238535,0.0000023409268,0.000030657407,0.96093076,0.030358303,0.00017659731,0.0047717397,0.00014888424],"about_ca_topic_score_codex":0.0000016624372,"about_ca_topic_score_gemma":8.098741e-7,"teacher_disagreement_score":0.955791,"about_ca_system_score_codex":0.000022502052,"about_ca_system_score_gemma":0.00003944132,"threshold_uncertainty_score":0.41085455},"labels":[],"label_agreement":null},{"id":"W3000158570","doi":"10.1109/apccas47518.2019.8953090","title":"Scale Invariant Super-Resolutions Methods with Application to InSAR Images","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Invariant (physics); Scale invariance; Artificial intelligence; Computer science; Scaling; Resolution (logic); Interferometric synthetic aperture radar; Image resolution; Scale (ratio); Ground truth; Artificial neural network; Pattern recognition (psychology); Image (mathematics); Synthetic aperture radar; Computer vision; Mathematics; Statistics; Geography; Cartography","score_opus":0.00946562425753176,"score_gpt":0.3193210526835716,"score_spread":0.3098554284260398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000158570","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039281163,0.000032206965,0.9907841,0.0026595502,0.000027277054,0.0003820335,9.185374e-7,0.00083697774,0.0048841084],"genre_scores_gemma":[0.034144416,0.000003091022,0.9636835,0.00084995833,0.000012601647,0.00010968288,0.0000012247782,0.0000113311835,0.00118418],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99901325,0.00004982865,0.00013419775,0.00043607238,0.00015342653,0.00021321636],"domain_scores_gemma":[0.9988788,0.000053701202,0.0000334814,0.0008059344,0.00014395619,0.00008415578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029341757,0.000111544105,0.00012409099,0.00011290555,0.000087899214,0.000091886584,0.00077273767,0.00003339035,0.000013966669],"category_scores_gemma":[0.000027806036,0.00007649982,0.000018748682,0.00062419474,0.000035178477,0.00088390685,0.00032794467,0.00009264452,0.00016734522],"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":[0.0000110057235,0.00010399455,0.00087614876,0.000025292002,0.00000841829,0.0000016117385,0.00044402224,0.00017612663,0.5970175,0.0302495,0.0020061214,0.36908022],"study_design_scores_gemma":[0.00037500184,0.0003444215,0.0030964247,0.00006207581,0.000009444981,0.000056474328,0.00006658765,0.23188993,0.69556975,0.03247034,0.035387054,0.0006724654],"about_ca_topic_score_codex":0.00006408057,"about_ca_topic_score_gemma":0.000013794945,"teacher_disagreement_score":0.36840776,"about_ca_system_score_codex":0.000047014433,"about_ca_system_score_gemma":0.000060868344,"threshold_uncertainty_score":0.3119572},"labels":[],"label_agreement":null},{"id":"W3002486569","doi":"10.48550/arxiv.2001.07766","title":"Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Task (project management); Image (mathematics); Process (computing); Function (biology); Pattern recognition (psychology); Pixel; Resolution (logic); Image resolution; Regular polygon; Computer vision; Mathematics","score_opus":0.10480154784559423,"score_gpt":0.22083864564572342,"score_spread":0.11603709780012919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002486569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004436794,0.000101748024,0.9962986,0.00013513089,0.00037813274,0.00083274266,0.0000118051785,0.0016956009,0.00010253091],"genre_scores_gemma":[0.52898777,0.000051385978,0.4705759,0.00015235848,0.00012854881,0.0000060036577,0.000033932425,0.000029183764,0.000034943987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978704,0.00012756592,0.0002592554,0.001273944,0.00009565622,0.00037319734],"domain_scores_gemma":[0.9981822,0.000080569625,0.00039777902,0.0007049209,0.0005150763,0.00011946456],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020413296,0.0003723025,0.0003576467,0.00023668219,0.00033887703,0.00018120024,0.0010837208,0.00040845093,0.0000039640186],"category_scores_gemma":[0.00005973094,0.0004640794,0.00018195566,0.00064204645,0.0001544559,0.0013364577,0.001373482,0.0005901415,0.0000012149247],"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":[0.00014488629,0.00002881371,0.00006413318,0.000055819815,0.000032461478,0.000028702012,0.000054164513,0.9803941,0.00013068796,0.01758778,0.00012859884,0.0013498244],"study_design_scores_gemma":[0.00020065966,0.00015155532,0.000010811083,0.00010692916,0.00008239945,0.0000054899606,0.00001967736,0.9533561,0.00041233384,0.04513556,0.000078105935,0.00044041796],"about_ca_topic_score_codex":0.00003539273,"about_ca_topic_score_gemma":0.0000028601441,"teacher_disagreement_score":0.52854407,"about_ca_system_score_codex":0.00041461512,"about_ca_system_score_gemma":0.00014768308,"threshold_uncertainty_score":0.9997811},"labels":[],"label_agreement":null},{"id":"W3004211383","doi":"10.1109/globalsip45357.2019.8969481","title":"Wide Separate 3D Convolution for Video Super Resolution","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Convolution (computer science); Computer science; Artificial intelligence; Convolutional neural network; Computer vision; Motion estimation; Computation; Frame (networking); Image resolution; Motion compensation; Ground truth; Domain (mathematical analysis); Compensation (psychology); Algorithm; Artificial neural network; Mathematics; Telecommunications","score_opus":0.012439632722790544,"score_gpt":0.2759416393883894,"score_spread":0.26350200666559886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004211383","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014783153,0.00010276521,0.9919651,0.00060681254,0.00018328085,0.00035295248,0.0000010063945,0.00083222723,0.0044775456],"genre_scores_gemma":[0.09637122,0.0000041861913,0.899501,0.0009783481,0.000022477914,0.000049668146,0.0000028681964,0.000009205583,0.0030610084],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911195,0.000020201505,0.0001544303,0.00034557993,0.00012638782,0.00024144293],"domain_scores_gemma":[0.999229,0.00009136068,0.000058991103,0.00042248509,0.00016083784,0.00003728675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023286439,0.00009743661,0.000106911335,0.00006509949,0.000079179124,0.00009292579,0.00041572354,0.000042158706,0.000019606947],"category_scores_gemma":[0.00008050118,0.00008842811,0.00003760498,0.00017405214,0.000028803735,0.001363613,0.00013060386,0.00006203405,0.00015560977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014636427,0.00019600544,0.0047566504,0.00027035316,0.000038428734,0.0000056901895,0.0006536753,0.0006047784,0.30057296,0.5344536,0.09524325,0.06305829],"study_design_scores_gemma":[0.00038872444,0.00014666733,0.00030500258,0.0000321664,0.0000034613022,0.000008599688,0.000006900692,0.8210472,0.039680988,0.06884897,0.0692821,0.00024924826],"about_ca_topic_score_codex":0.000009925817,"about_ca_topic_score_gemma":0.0000026872847,"teacher_disagreement_score":0.8204424,"about_ca_system_score_codex":0.000069782516,"about_ca_system_score_gemma":0.000054511776,"threshold_uncertainty_score":0.36059937},"labels":[],"label_agreement":null},{"id":"W3004871661","doi":"10.1109/pacrim47961.2019.8985104","title":"Video Super-Resolution with Compensation in Feature Extraction","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Motion compensation; Pixel; Computer vision; Compensation (psychology); Optical flow; Residual; Feature extraction; Convolutional neural network; Feature (linguistics); Frame (networking); Bilateral filter; Motion estimation; Pattern recognition (psychology); Filter (signal processing); Image (mathematics); Algorithm","score_opus":0.008321808279812729,"score_gpt":0.2604784953613171,"score_spread":0.2521566870815044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004871661","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01306263,0.000043686854,0.98084,0.0012566366,0.000058490383,0.00017546638,1.1022405e-7,0.0004280228,0.004134904],"genre_scores_gemma":[0.478854,0.000002365314,0.52044725,0.00012744557,0.0000071325335,0.0000071097065,0.000001520245,0.000003789274,0.0005493742],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993386,0.000023710685,0.00008352009,0.00025833526,0.0001599141,0.00013587826],"domain_scores_gemma":[0.99955964,0.000028784334,0.00004317645,0.0002811041,0.00006705111,0.000020251866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012225691,0.00007806688,0.00007762545,0.00010820821,0.000031166885,0.000082103,0.00022893086,0.000047272028,0.000014000938],"category_scores_gemma":[0.000012040923,0.000062760635,0.000010575937,0.0003362276,0.000014238316,0.001765766,0.000049589868,0.00013990486,0.000042357635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018450794,0.00042943333,0.07759606,0.00014773369,0.000015614563,0.00004820414,0.0017710832,0.0024353685,0.40141648,0.2760221,0.0053793024,0.2345541],"study_design_scores_gemma":[0.0010324606,0.00036095857,0.07023688,0.00021061674,0.000003324962,0.0001230023,0.00008773261,0.8288983,0.069541834,0.02123992,0.0077103972,0.0005545426],"about_ca_topic_score_codex":0.000028831899,"about_ca_topic_score_gemma":0.000047534057,"teacher_disagreement_score":0.826463,"about_ca_system_score_codex":0.000083500454,"about_ca_system_score_gemma":0.000031541163,"threshold_uncertainty_score":0.25593042},"labels":[],"label_agreement":null},{"id":"W3009868028","doi":"10.1007/s10489-020-01670-y","title":"Residual learning based densely connected deep dilated network for joint deblocking and super resolution","year":2020,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Deblocking filter; Residual; Artificial intelligence; JPEG; Convolution (computer science); Joint (building); Computer vision; Deep learning; Image compression; Compression artifact; Image (mathematics); Algorithm; Image processing; Artificial neural network","score_opus":0.037878825805533214,"score_gpt":0.25492127613451615,"score_spread":0.21704245032898295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009868028","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016252745,0.00035668953,0.99527526,0.0011242235,0.00004094011,0.00041891463,7.4770463e-7,0.0010292006,0.00012872576],"genre_scores_gemma":[0.46680197,0.00001675172,0.5323449,0.00069121236,0.00006062477,0.000057393216,0.0000054921525,0.000017282646,0.0000044008466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830616,0.0000489461,0.0003495182,0.0006621299,0.00019439288,0.00043886984],"domain_scores_gemma":[0.9989716,0.0003258207,0.0001483801,0.000251754,0.00017076916,0.00013167292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033611106,0.00021016148,0.00023115215,0.000057339555,0.0003593619,0.00018387097,0.0004784402,0.000100167206,0.0000059805266],"category_scores_gemma":[0.00044482754,0.00021700741,0.000034973462,0.0005475118,0.00010397793,0.00026955723,0.00024274256,0.0003039707,0.000008769472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005899389,0.000102774866,0.00083292177,0.00042587734,0.00008477003,0.00004102412,0.0074070236,0.18584134,0.27601045,0.09185526,0.0027028767,0.43410575],"study_design_scores_gemma":[0.000110027235,0.00013211014,0.00006419907,0.000049794286,0.000008819478,0.0000047626877,0.000047780948,0.85967386,0.12530108,0.013261879,0.0010852786,0.0002603904],"about_ca_topic_score_codex":0.0000060222233,"about_ca_topic_score_gemma":0.0000038298595,"teacher_disagreement_score":0.67383254,"about_ca_system_score_codex":0.00003991953,"about_ca_system_score_gemma":0.0000594423,"threshold_uncertainty_score":0.88493055},"labels":[],"label_agreement":null},{"id":"W3011688396","doi":"10.3390/rs12091432","title":"Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":289,"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 Energy; Athabasca University; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Detector; Computer science; Residual; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Computer vision; Generative adversarial network; Context (archaeology); Object detection; Overhead (engineering); Image resolution; Remote sensing; Deep learning; Pattern recognition (psychology); Telecommunications; Algorithm","score_opus":0.016381683787962,"score_gpt":0.24478289104877646,"score_spread":0.22840120726081445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011688396","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07111364,0.00027788544,0.9258798,0.00069454557,0.00013148949,0.00040213222,5.4552066e-7,0.001026739,0.00047325608],"genre_scores_gemma":[0.48210868,0.000023993398,0.51711416,0.00055817247,0.00014133655,1.8886666e-8,4.794491e-7,0.000041820418,0.000011344274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970614,0.00022211677,0.00043408028,0.001153329,0.00034275083,0.0007863578],"domain_scores_gemma":[0.99851567,0.00022029037,0.00024198713,0.00059238245,0.00020012635,0.00022952349],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044873563,0.0004387387,0.00051803346,0.00024581925,0.00033130808,0.00042610118,0.00031682354,0.00013372907,6.6668747e-7],"category_scores_gemma":[0.00042728562,0.00043728258,0.00005992942,0.0017257757,0.00012385512,0.00059304223,0.00033053858,0.0005836948,0.0000063776447],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055830074,0.0000016535796,0.0000016628012,0.000039857678,0.000008164203,0.00012392903,0.0007441423,0.0006336356,0.28143528,0.0000010947784,0.0000060209613,0.71694875],"study_design_scores_gemma":[0.00027431193,0.000184765,0.00010510932,0.0005296776,0.00001188827,0.00022749993,0.000040141545,0.5841937,0.4128544,0.00104078,0.00013188687,0.00040585024],"about_ca_topic_score_codex":0.00021057027,"about_ca_topic_score_gemma":0.00032792936,"teacher_disagreement_score":0.7165429,"about_ca_system_score_codex":0.00018091429,"about_ca_system_score_gemma":0.00011986993,"threshold_uncertainty_score":0.9998079},"labels":[],"label_agreement":null},{"id":"W3012290642","doi":"10.3390/app10051865","title":"Image Magnification Based on Bicubic Approximation with Edge as Constraint","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Bicubic interpolation; Mathematics; Polynomial; Magnification; Piecewise; Surface (topology); Image (mathematics); Image gradient; Enhanced Data Rates for GSM Evolution; Algorithm; Artificial intelligence; Computer vision; Edge detection; Computer science; Image processing; Linear interpolation; Geometry; Mathematical analysis","score_opus":0.023020520260847024,"score_gpt":0.26590670685000095,"score_spread":0.24288618658915392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012290642","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015994736,0.000008665378,0.96086305,0.0047249505,0.00002033956,0.0003074595,8.430954e-7,0.0006790561,0.031796183],"genre_scores_gemma":[0.48686895,8.145815e-7,0.5113795,0.0016826887,0.000015050103,0.000044095093,0.000001317475,0.0000040949258,0.000003491603],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984596,0.000017958959,0.0001649417,0.00063146633,0.0004904317,0.00023564596],"domain_scores_gemma":[0.9993006,0.00008405849,0.00014779987,0.0002944721,0.00007263434,0.00010040726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032723197,0.00014697252,0.0001180271,0.00010258955,0.00028427088,0.00034386056,0.0009914958,0.000032509328,0.00001283856],"category_scores_gemma":[0.00006866722,0.00011260556,0.000018609879,0.0010805202,0.00057102606,0.00063452474,0.00007512841,0.00012107336,0.0001043904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006246949,0.00015981683,0.000078114674,0.00010905155,0.0000044535827,0.000013217775,0.0012098389,0.0007541123,0.31746835,0.5724738,0.00074971246,0.10691704],"study_design_scores_gemma":[0.00031472408,0.00056553487,0.00012295227,0.000033198987,0.000004584407,0.000006451145,0.00015300565,0.77094203,0.19422476,0.03291671,0.0003783002,0.00033777117],"about_ca_topic_score_codex":0.0000018364449,"about_ca_topic_score_gemma":2.7387648e-7,"teacher_disagreement_score":0.7701879,"about_ca_system_score_codex":0.000027279533,"about_ca_system_score_gemma":0.00020574068,"threshold_uncertainty_score":0.45919213},"labels":[],"label_agreement":null},{"id":"W3013947696","doi":"10.18280/isi.250111","title":"Performance Evaluation of Generative Adversarial Networks for Computer Vision Applications","year":2020,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Adversarial system; Generative grammar; Computer science; Artificial intelligence; Generative adversarial network; Computer vision; Human–computer interaction; Machine learning; Deep learning","score_opus":0.02596047291657484,"score_gpt":0.285848169300868,"score_spread":0.25988769638429315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013947696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021460056,0.000060435978,0.99568737,0.00014019173,0.000113723734,0.0010573689,0.000006461868,0.00026222138,0.0005262159],"genre_scores_gemma":[0.5067287,0.000008915985,0.49262336,0.00021335203,0.00010837709,0.00026569585,0.000046137848,0.000004623054,8.2015276e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987807,0.000051117022,0.00048769504,0.00016783884,0.00034674443,0.00016588144],"domain_scores_gemma":[0.9980914,0.000056319295,0.0004302917,0.00023484605,0.0011311227,0.000056005076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005848307,0.00012776307,0.00016420145,0.000095327734,0.00021431355,0.00014582806,0.00043053814,0.000072523384,0.0000027692975],"category_scores_gemma":[0.00010222721,0.00012680542,0.000050255047,0.000448596,0.00007925004,0.004903307,0.00013347207,0.0000776378,0.0000061616593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036638714,0.000015743726,0.000051752028,0.00017188005,0.0000140951015,3.564539e-8,0.0026425899,0.07070164,0.00062401994,0.0039656814,0.00052377785,0.92125213],"study_design_scores_gemma":[0.00044096148,0.00024308726,0.0002579677,0.00005152426,0.000015528525,0.0000024120977,0.00002881024,0.991607,0.0036309278,0.0024803977,0.0011085122,0.00013283701],"about_ca_topic_score_codex":0.0000015464616,"about_ca_topic_score_gemma":2.5598436e-7,"teacher_disagreement_score":0.92111933,"about_ca_system_score_codex":0.00013706727,"about_ca_system_score_gemma":0.00012698653,"threshold_uncertainty_score":0.51709753},"labels":[],"label_agreement":null},{"id":"W3014210955","doi":"10.1109/iceic49074.2020.9051244","title":"MSG-CapsGAN: Multi-Scale Gradient Capsule GAN for Face Super Resolution","year":2020,"lang":"en","type":"article","venue":"2020 International Conference on Electronics, Information, and Communication (ICEIC)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Discriminator; Computer science; Face (sociological concept); Artificial intelligence; Computer vision; Image resolution; Scale (ratio); Facial recognition system; Distortion (music); Image (mathematics); Process (computing); High resolution; Resolution (logic); Pattern recognition (psychology); Bandwidth (computing); Telecommunications; Detector; Physics","score_opus":0.036074979337333196,"score_gpt":0.288467157043592,"score_spread":0.25239217770625877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014210955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007207319,0.00042908778,0.9696896,0.024843587,0.00008861836,0.00044453773,0.000030194406,0.00035135815,0.003402274],"genre_scores_gemma":[0.75051814,0.0025087139,0.24235603,0.0037838907,0.000047819136,0.00020523026,0.0003987719,0.0000144316855,0.00016699333],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851286,0.000065847664,0.0005257879,0.00028604842,0.00032597184,0.00028350702],"domain_scores_gemma":[0.99816,0.000058231682,0.00032399406,0.00054312916,0.0007900673,0.00012459804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030132348,0.00020612279,0.00017837713,0.000107865955,0.00035769722,0.00045790733,0.0015850383,0.00008911088,0.000021903981],"category_scores_gemma":[0.00019230791,0.00021629012,0.000060193237,0.00022099007,0.00009652023,0.0027281905,0.00023845615,0.00032682277,0.000042076994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001479781,0.00020886862,0.00016201139,0.00010409581,0.00009228746,4.179922e-7,0.015106597,0.00061779533,0.010746637,0.90199333,0.009141579,0.06167838],"study_design_scores_gemma":[0.00074393005,0.00021922415,0.00025440313,0.000046043195,0.000008177637,0.0000075242,0.00043307862,0.9179804,0.0058184494,0.01290301,0.061293215,0.00029258977],"about_ca_topic_score_codex":0.000025118354,"about_ca_topic_score_gemma":0.000039394883,"teacher_disagreement_score":0.9173626,"about_ca_system_score_codex":0.00018649163,"about_ca_system_score_gemma":0.00021548763,"threshold_uncertainty_score":0.8820055},"labels":[],"label_agreement":null},{"id":"W3021882766","doi":"10.1109/jstars.2020.2984589","title":"An Efficient Deep Unsupervised Superresolution Model for Remote Sensing Images","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Feature (linguistics); Block (permutation group theory); Field (mathematics); Superresolution; Convolutional neural network; Feature extraction; Pattern recognition (psychology); Unsupervised learning; Reduction (mathematics); Image (mathematics)","score_opus":0.039124235446242606,"score_gpt":0.2711624804410195,"score_spread":0.23203824499477688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021882766","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061177053,0.00015089645,0.936492,0.0017164801,0.00008871582,0.00022828937,0.0000015022662,0.00011530948,0.000029757823],"genre_scores_gemma":[0.2729954,0.000050620485,0.7263739,0.00043236947,0.00012613535,2.4131648e-8,0.0000018937864,0.000015491598,0.000004142238],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998462,0.000050280232,0.0005741369,0.00032172492,0.0002911102,0.00030069682],"domain_scores_gemma":[0.998512,0.000083761624,0.0002769116,0.0002216415,0.0007860039,0.000119665296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038503122,0.00018386442,0.0003013664,0.00019349181,0.0002482861,0.00019333274,0.00024782374,0.000108126536,1.446377e-7],"category_scores_gemma":[0.0001916518,0.00018026045,0.000048299346,0.00077654637,0.000058397127,0.0003549375,0.000043145315,0.00038338493,1.3662223e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043703927,0.000017256756,0.000004376949,0.000054029726,0.000012007185,0.000013010539,0.0015941365,0.09396622,0.30175442,0.00024654152,0.000017456532,0.60227686],"study_design_scores_gemma":[0.0005234926,0.000077902376,0.00017966666,0.00009780205,0.0000145423255,0.00005584278,0.00004725185,0.9717528,0.019529829,0.00743191,0.00009957313,0.00018934917],"about_ca_topic_score_codex":0.000011618768,"about_ca_topic_score_gemma":0.000017719527,"teacher_disagreement_score":0.87778664,"about_ca_system_score_codex":0.00006222522,"about_ca_system_score_gemma":0.00020261803,"threshold_uncertainty_score":0.7350808},"labels":[],"label_agreement":null},{"id":"W3023821242","doi":"10.20944/preprints202003.0313.v2","title":"Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network","year":2020,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Energy; Athabasca University; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Detector; Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Residual; Computer vision; Context (archaeology); Overhead (engineering); Image resolution; Object detection; Generative adversarial network; Deep learning; Pattern recognition (psychology); Telecommunications; Algorithm","score_opus":0.05712182583916333,"score_gpt":0.3082877557819736,"score_spread":0.2511659299428103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023821242","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1716924,0.00023154498,0.823715,0.00041240428,0.00031688545,0.0010743487,0.000003225981,0.001496422,0.0010578178],"genre_scores_gemma":[0.62397,0.00008780181,0.37539998,0.00022654692,0.00015817603,0.000027487626,0.0000027710444,0.00007290278,0.00005435321],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9946263,0.00037702778,0.00074991665,0.0028467032,0.0005062278,0.0008938157],"domain_scores_gemma":[0.99656296,0.00023129887,0.00058524543,0.002040605,0.00030220358,0.00027771134],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010233095,0.00081048487,0.0009282608,0.00042928947,0.0002605484,0.0003143884,0.0014938024,0.0003732877,0.000010726193],"category_scores_gemma":[0.0007290267,0.00084520684,0.00012695398,0.0011490239,0.00019439013,0.0005112548,0.004586654,0.0019328338,0.0000550093],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031100822,0.00004147763,0.003447966,0.0006007467,0.00010895302,0.0002494792,0.0031028956,0.0036666896,0.40769765,0.000021637265,0.000009765554,0.5807417],"study_design_scores_gemma":[0.00043554936,0.00014362128,0.029550884,0.001655224,0.000042137508,0.0001009544,0.00003337047,0.055944752,0.8962991,0.0143862665,0.00020868072,0.0011994614],"about_ca_topic_score_codex":0.00040118484,"about_ca_topic_score_gemma":0.00037941724,"teacher_disagreement_score":0.5795423,"about_ca_system_score_codex":0.0003811974,"about_ca_system_score_gemma":0.00034307965,"threshold_uncertainty_score":0.9993999},"labels":[],"label_agreement":null},{"id":"W3024189128","doi":"10.1109/vrw50115.2020.00259","title":"Panoramic Image Quality-Enhancement by Fusing Neural Textures of the Adaptive Initial Viewport","year":2020,"lang":"en","type":"article","venue":"2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Viewport; Computer science; Computer vision; Image quality; Virtual reality; Artificial intelligence; Image (mathematics)","score_opus":0.06194365263380987,"score_gpt":0.33410200211366314,"score_spread":0.2721583494798533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024189128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4801497,0.0005983791,0.50399655,0.012477527,0.00035024382,0.0005867389,0.00010560758,0.00023053863,0.0015047122],"genre_scores_gemma":[0.99173576,0.00036960715,0.0059690047,0.0017551361,0.000055936795,0.000013196424,0.000005248341,0.000014838319,0.000081297614],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99767154,0.00020548754,0.00066500733,0.00071457896,0.00041312288,0.00033028805],"domain_scores_gemma":[0.99849796,0.0002176154,0.00049706805,0.00040947951,0.00018896798,0.00018887714],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003795745,0.0003451012,0.0004483288,0.000030098912,0.00023347467,0.00042450713,0.00077346433,0.00013922919,0.000018510478],"category_scores_gemma":[0.00019081487,0.00025398252,0.00006224531,0.00023124475,0.0004904008,0.00085587276,0.00044521253,0.00067405857,0.0000031240131],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088785205,0.00050441275,0.00015884277,0.00039158008,0.0001578175,0.00004025406,0.0113272155,0.0003761603,0.36870953,0.012154632,0.007750576,0.5975411],"study_design_scores_gemma":[0.0024021184,0.0036115472,0.009076612,0.0025515426,0.00015176697,0.00004681276,0.004214705,0.1761261,0.7818485,0.012753279,0.0045596384,0.0026573737],"about_ca_topic_score_codex":0.00010933271,"about_ca_topic_score_gemma":0.000033627435,"teacher_disagreement_score":0.59488374,"about_ca_system_score_codex":0.000026272439,"about_ca_system_score_gemma":0.00009949907,"threshold_uncertainty_score":0.99999124},"labels":[],"label_agreement":null},{"id":"W3032230032","doi":"10.1007/s11263-020-01325-y","title":"Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation","year":2020,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Image (mathematics); Artificial intelligence; Image synthesis; Generative grammar; Graphics; Computer graphics; Computer vision; Estimation; Superresolution; Image processing; Pattern recognition (psychology); Computer graphics (images)","score_opus":0.007829632401208465,"score_gpt":0.2870485113665846,"score_spread":0.2792188789653761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3032230032","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027329025,0.000031462605,0.98553413,0.011134288,0.00026527728,0.000074556185,0.0000069913567,0.00013922196,0.00008118286],"genre_scores_gemma":[0.390543,0.00000518258,0.6082898,0.00087041856,0.00027512127,0.0000023772532,0.0000032993116,0.000009988562,8.159456e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981145,0.00005322077,0.00049265154,0.0002644509,0.00090698287,0.0001681563],"domain_scores_gemma":[0.99779165,0.00018212703,0.0005669772,0.00015857251,0.0011409279,0.00015975631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020657187,0.00017342264,0.00022488732,0.00021061477,0.00006523344,0.00042219,0.0014980808,0.00004655543,0.000017521554],"category_scores_gemma":[0.00009778336,0.00013993631,0.00010218621,0.00018726847,0.00006149233,0.0025668114,0.00031186198,0.00024445762,0.000024418423],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003932669,0.00032298797,0.00017593619,0.000032719963,0.00020471375,0.00077785266,0.00059627264,0.004344142,0.0406059,0.004780293,0.0042378637,0.94352806],"study_design_scores_gemma":[0.00081560086,0.0007083052,0.0018243121,0.00025210663,0.000014678063,0.0009951168,0.000006014133,0.9287768,0.018986315,0.04632525,0.0010607437,0.0002347359],"about_ca_topic_score_codex":0.0000020049551,"about_ca_topic_score_gemma":1.7115285e-7,"teacher_disagreement_score":0.94329333,"about_ca_system_score_codex":0.00009178558,"about_ca_system_score_gemma":0.00012981641,"threshold_uncertainty_score":0.57064366},"labels":[],"label_agreement":null},{"id":"W3034419329","doi":"10.1109/cvpr42600.2020.00753","title":"Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":381,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Inpainting; Computer science; Residual; Artificial intelligence; Convolutional neural network; Inference; Image (mathematics); Computer vision; Deep learning; Image resolution; Pattern recognition (psychology); Algorithm","score_opus":0.024481091008702566,"score_gpt":0.2774175768975865,"score_spread":0.25293648588888396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034419329","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007351018,0.00004113362,0.9891796,0.007697964,0.00005031126,0.00025243894,0.0000027883382,0.001358082,0.00068258593],"genre_scores_gemma":[0.28803417,0.0000024943854,0.71052897,0.0012363939,0.00010130539,0.000028085024,0.0000056277077,0.000009011765,0.000053937416],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989861,0.000032730375,0.00022031821,0.0003702604,0.00016478026,0.00022580917],"domain_scores_gemma":[0.99928564,0.00013088928,0.00011927355,0.00019849274,0.00019729469,0.00006842993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022635682,0.00011066921,0.00012061833,0.000043147393,0.00015072072,0.00019582151,0.00048925576,0.000045278695,0.00000556926],"category_scores_gemma":[0.0007430963,0.0001066298,0.000034019788,0.00023308354,0.00005141657,0.0014002558,0.00010646198,0.00009508207,0.000014903119],"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":[0.000056337296,0.000050944385,0.0000670789,0.00011989805,0.000014822808,0.000007729169,0.0016930213,0.0000527271,0.50158936,0.22025573,0.019560715,0.25653166],"study_design_scores_gemma":[0.00062432955,0.00026245276,0.0000885915,0.00004448559,0.0000061660867,0.0000060334783,0.000080504455,0.38105363,0.5736465,0.04136652,0.0025012428,0.00031958573],"about_ca_topic_score_codex":0.000013689701,"about_ca_topic_score_gemma":0.000002269833,"teacher_disagreement_score":0.3810009,"about_ca_system_score_codex":0.000034160927,"about_ca_system_score_gemma":0.000039676594,"threshold_uncertainty_score":0.4348237},"labels":[],"label_agreement":null},{"id":"W3034427410","doi":"10.1109/icme46284.2020.9102951","title":"Srnmfrb: A Deep Light-Weight Super Resolution Network Using Multi-Receptive Field Feature Generation Residual Blocks","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Residual; Computer science; Convolution (computer science); Block (permutation group theory); Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Artificial neural network; Convolutional neural network; Deep learning; Field (mathematics); Receptive field; Algorithm; Computer vision; Mathematics; Geometry","score_opus":0.04071541617539724,"score_gpt":0.28264151809820093,"score_spread":0.24192610192280367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034427410","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00081918127,0.000612597,0.9859921,0.010849828,0.00019697004,0.0002186527,7.7917645e-7,0.00087985425,0.0004300913],"genre_scores_gemma":[0.042465817,0.00002256581,0.9520664,0.004233159,0.00091620727,0.000015072021,0.0000066394937,0.000018963854,0.00025513134],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839944,0.0000919551,0.0002296321,0.0006230719,0.00027233586,0.00038357574],"domain_scores_gemma":[0.9991564,0.000049290302,0.00010198389,0.00036729826,0.00019881366,0.00012619447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015494917,0.0002165123,0.00018813732,0.00005335131,0.00032906214,0.00023029251,0.0005838222,0.00018366768,0.000029613137],"category_scores_gemma":[0.00015724616,0.0001941984,0.00005529146,0.0005822776,0.000029518436,0.001128425,0.00037738745,0.00034263264,0.000012941847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015465239,0.00024591575,0.0015862975,0.0000665724,0.000110967645,0.00013055363,0.010379084,0.013436466,0.57987374,0.012369509,0.31299153,0.06865473],"study_design_scores_gemma":[0.00020675908,0.0001124827,0.000026618405,0.000028427521,0.000009560597,0.000012848302,0.000018336836,0.8755288,0.11873244,0.0007444697,0.0043109152,0.00026835903],"about_ca_topic_score_codex":0.000014927947,"about_ca_topic_score_gemma":0.000030970445,"teacher_disagreement_score":0.8620923,"about_ca_system_score_codex":0.000076944154,"about_ca_system_score_gemma":0.00007359555,"threshold_uncertainty_score":0.7919181},"labels":[],"label_agreement":null},{"id":"W3034818018","doi":"10.1109/icme46284.2020.9102784","title":"MGHCNET: A Deep Multi-Scale Granular and Holistic Channel Feature Generation Network for Image Super Resolution","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Residual; Feature (linguistics); Computer science; Block (permutation group theory); Benchmark (surveying); Artificial intelligence; Channel (broadcasting); Scale (ratio); Image (mathematics); Pattern recognition (psychology); Feature extraction; Algorithm; Mathematics; Telecommunications","score_opus":0.046575902014695414,"score_gpt":0.29000185792276506,"score_spread":0.24342595590806965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034818018","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015674792,0.0008583965,0.99412745,0.0038337766,0.00008218868,0.00036255943,0.0000038275425,0.00053735514,0.00003767579],"genre_scores_gemma":[0.022545358,0.00003279553,0.9750397,0.0018452674,0.0003280315,0.00007232798,0.00001958628,0.000016399148,0.00010052716],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901354,0.000029535067,0.00013010857,0.00045263307,0.000112837886,0.0002613622],"domain_scores_gemma":[0.99947155,0.000026649615,0.00004944913,0.00021836351,0.00013275802,0.00010121577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013076114,0.00014154617,0.0001390915,0.000028830413,0.00022124383,0.00020452404,0.00027897645,0.000077509736,0.0000015770389],"category_scores_gemma":[0.00009083053,0.00012848317,0.0000346206,0.00022743753,0.000048180853,0.0007987945,0.00015502519,0.0001095208,0.0000028987743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016685971,0.00025458273,0.000413404,0.00061715854,0.00004576132,0.00005463968,0.0084300125,0.0033190676,0.722646,0.019459223,0.14856918,0.0960241],"study_design_scores_gemma":[0.00030132273,0.000082382605,0.00011874577,0.000011159804,0.0000073568476,0.000009655188,0.000011176187,0.98754394,0.005876914,0.004524153,0.0013424,0.00017081464],"about_ca_topic_score_codex":0.000004677785,"about_ca_topic_score_gemma":0.000023263767,"teacher_disagreement_score":0.98422486,"about_ca_system_score_codex":0.000021059288,"about_ca_system_score_gemma":0.000017988032,"threshold_uncertainty_score":0.5239392},"labels":[],"label_agreement":null},{"id":"W3036864681","doi":"10.1007/978-3-030-50347-5_6","title":"4K or Not? - Automatic Image Resolution Assessment","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Ground truth; Feature (linguistics); Resolution (logic); Computer vision; Image quality; Popularity; Enhanced Data Rates for GSM Evolution; High resolution; Image (mathematics); Computer graphics (images); Remote sensing","score_opus":0.023779425741770343,"score_gpt":0.30771856394440034,"score_spread":0.28393913820263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036864681","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000002696597,0.00013882741,0.9919861,0.0029177968,0.00076629693,0.0005195801,0.000005227389,0.0013452852,0.0023181883],"genre_scores_gemma":[0.008590761,0.000031226642,0.9876762,0.0030985437,0.0002779746,0.000024992434,0.000004671681,0.00005203808,0.00024358649],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99508053,0.000053551204,0.00071295846,0.00202768,0.0014049695,0.0007202928],"domain_scores_gemma":[0.99679404,0.00043973236,0.0005087554,0.0017024785,0.00032473556,0.00023023988],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00083106867,0.000664987,0.0006764737,0.00069377467,0.0003566752,0.001087709,0.0047798743,0.0002915313,0.000031257146],"category_scores_gemma":[0.00029432276,0.0005657409,0.0001282958,0.0009371401,0.000866475,0.001820842,0.002734583,0.0012176353,0.00006103],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001153695,0.000034944278,0.0000029519813,0.0001713863,0.000010232214,0.00039214315,0.00038320204,0.0013553789,0.0023080239,0.015849251,0.00016110072,0.9793199],"study_design_scores_gemma":[0.00016300066,0.00022418176,0.00004875005,0.0005424146,0.000007793366,0.000095556235,8.213013e-8,0.840066,0.0026745712,0.15459535,0.00095597474,0.0006263306],"about_ca_topic_score_codex":0.000006612818,"about_ca_topic_score_gemma":0.0000117175905,"teacher_disagreement_score":0.97869354,"about_ca_system_score_codex":0.00075369916,"about_ca_system_score_gemma":0.001427452,"threshold_uncertainty_score":0.9999493},"labels":[],"label_agreement":null},{"id":"W3046774880","doi":"10.37190/oa200202","title":"Deblurring approach for motion camera combining FFT with α-confidence goal optimization","year":2020,"lang":"en","type":"article","venue":"Optica Applicata","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Deblurring; Artificial intelligence; Computer vision; Deconvolution; Computer science; Motion blur; Image restoration; Image (mathematics); Image processing; Algorithm","score_opus":0.020435948992165304,"score_gpt":0.25489009268060914,"score_spread":0.23445414368844383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046774880","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000038480277,0.000046054785,0.99630916,0.0010834103,0.000009870354,0.00062222785,0.000002721685,0.00077597966,0.0011121043],"genre_scores_gemma":[0.12421292,0.000006068029,0.87485904,0.00046733808,0.00004053527,0.00035806448,0.000021259892,0.00002488442,0.000009896906],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986234,0.000013837764,0.00020020967,0.00067782385,0.00020348103,0.00028125668],"domain_scores_gemma":[0.99903786,0.000057650763,0.0001314215,0.000510679,0.00013308525,0.00012929001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013443068,0.00017161896,0.0001833879,0.000039030932,0.00018195338,0.00024181728,0.0009987211,0.000053774147,0.0000017798055],"category_scores_gemma":[0.00006647096,0.00016173799,0.000024759775,0.00041253725,0.00007904093,0.0007973805,0.00026869396,0.00014464841,0.0000044526573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027232608,0.00053783314,0.00030733424,0.0008977046,0.000101806385,0.000016080632,0.003081138,0.20316081,0.09559705,0.49253362,0.0013610878,0.2021332],"study_design_scores_gemma":[0.0002757121,0.00013041636,0.0000050887425,0.000021438576,0.000010814632,0.00001277634,0.000027508842,0.9900141,0.007792809,0.0012105164,0.00028304616,0.00021577206],"about_ca_topic_score_codex":0.0000030008666,"about_ca_topic_score_gemma":1.0137247e-7,"teacher_disagreement_score":0.7868533,"about_ca_system_score_codex":0.0000326592,"about_ca_system_score_gemma":0.000047077432,"threshold_uncertainty_score":0.65954834},"labels":[],"label_agreement":null},{"id":"W3056423227","doi":"10.1007/s11042-020-09489-y","title":"Capsule GAN for robust face super resolution","year":2020,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Artificial intelligence; Face (sociological concept); Image (mathematics); Distortion (music); Similarity (geometry); Metric (unit); Feature (linguistics); Pattern recognition (psychology); Facial recognition system; Domain (mathematical analysis); Computer vision; Superresolution; Field (mathematics); Mathematics; Telecommunications","score_opus":0.06010338682795666,"score_gpt":0.2861199175067022,"score_spread":0.22601653067874553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3056423227","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000056206067,0.00023092079,0.9929562,0.005488029,0.00001197713,0.00058969745,0.00003434835,0.0003986215,0.00023399832],"genre_scores_gemma":[0.028697968,0.00003711272,0.9695772,0.0006270127,0.00010747439,0.0008580963,0.000028293864,0.00000969515,0.000057145815],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993265,0.000007480031,0.00012433463,0.00032014534,0.00007283317,0.00014865628],"domain_scores_gemma":[0.9994987,0.00006214419,0.000042955224,0.00022202628,0.00007280357,0.00010132623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000056718378,0.00008332314,0.00008789245,0.00001858005,0.00017950719,0.00012694157,0.00031785268,0.00003903092,0.000002296616],"category_scores_gemma":[0.00007661543,0.00008126176,0.00002286791,0.000177995,0.000051537223,0.00044181675,0.000084630796,0.00006853266,0.000013160739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006488015,0.00008798803,0.00007108325,0.00009174749,0.000010320556,6.559235e-7,0.0015850825,0.00041186216,0.1219682,0.018791707,0.009223154,0.84775174],"study_design_scores_gemma":[0.00023151806,0.00003310324,0.00023335102,0.0000069181838,0.0000055288265,0.0000019289585,0.000037414004,0.89979523,0.012265839,0.0033574128,0.08387193,0.00015981971],"about_ca_topic_score_codex":0.0000027083927,"about_ca_topic_score_gemma":7.1113277e-7,"teacher_disagreement_score":0.89938337,"about_ca_system_score_codex":0.000012694234,"about_ca_system_score_gemma":0.000024551855,"threshold_uncertainty_score":0.3313758},"labels":[],"label_agreement":null},{"id":"W3082578808","doi":"10.1109/tci.2020.3019137","title":"Fast Multi-Focus Ultrasound Image Recovery Using Generative Adversarial Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Imaging","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Focus (optics); Computer science; Artificial intelligence; Frame rate; Computer vision; Image resolution; Frame (networking); Image (mathematics); Boundary (topology); Mean squared error; Deep learning; Image restoration; Image processing; Mathematics; Optics; Statistics; Telecommunications","score_opus":0.026554431178237543,"score_gpt":0.286255957119422,"score_spread":0.25970152594118445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082578808","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001708716,0.00007342714,0.9969253,0.0012002183,0.0005327596,0.00022235501,0.000022569913,0.00077923754,0.00007329201],"genre_scores_gemma":[0.32321712,0.0000062947897,0.67536813,0.0012307215,0.00012336271,0.000013203068,0.0000044924677,0.000026566056,0.000010099103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812716,0.000111226276,0.00037074392,0.00068317686,0.00035836073,0.0003493051],"domain_scores_gemma":[0.9988346,0.00031614944,0.00016769227,0.0002282472,0.0002870955,0.00016622651],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012463411,0.00029022846,0.00023024024,0.00016560227,0.00053629855,0.00045085908,0.0005361207,0.000051479332,0.00001939048],"category_scores_gemma":[0.000025607525,0.00032787322,0.0001389817,0.0006413665,0.00013482336,0.0021930048,0.000011279766,0.00044625794,0.000025194577],"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":[0.000027784941,0.00008969529,0.000005828554,0.000007365708,0.000030654337,0.000026944223,0.00042995787,0.9465801,0.01284777,0.00010761664,0.00011954192,0.0397267],"study_design_scores_gemma":[0.00061085547,0.000043850163,0.000017540913,0.00003596624,0.000018513652,0.00006845655,0.00003598668,0.9838972,0.012408105,0.0024977378,0.000027527107,0.00033827478],"about_ca_topic_score_codex":0.00002032664,"about_ca_topic_score_gemma":0.0000020808582,"teacher_disagreement_score":0.32304624,"about_ca_system_score_codex":0.00017535003,"about_ca_system_score_gemma":0.00016866275,"threshold_uncertainty_score":0.9999173},"labels":[],"label_agreement":null},{"id":"W3087750740","doi":"10.1007/978-3-030-67070-2_1","title":"AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results","year":2020,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Advanced Image Processing 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":"Centre for Social Innovation; McMaster University","funders":"","keywords":"Computer science; Image (mathematics); Focus (optics); Resolution (logic); Set (abstract data type); FLOPS; Magnification; Artificial intelligence; Factor (programming language); Superresolution; State (computer science); Algorithm; Parallel computing","score_opus":0.0374974150302877,"score_gpt":0.3556962747484532,"score_spread":0.3181988597181655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087750740","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002560117,0.0015033049,0.97578,0.019526662,0.001500752,0.0005253691,0.000008603457,0.00081031595,0.00008898913],"genre_scores_gemma":[0.21994573,0.00010729703,0.778221,0.0013813053,0.00028589898,0.00003474002,0.0000029720295,0.00002047153,5.7953184e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99392796,0.00032538304,0.00074840727,0.0032421316,0.00095427246,0.0008018438],"domain_scores_gemma":[0.9961978,0.0008467112,0.00036160517,0.0020246697,0.00027551595,0.0002937013],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0023518517,0.0006211418,0.00063189067,0.00050517474,0.0003564709,0.0009564329,0.004529904,0.0003215861,0.0000011993187],"category_scores_gemma":[0.0013836066,0.00056826486,0.0000987059,0.001900295,0.00082958286,0.00039998256,0.008860965,0.0017720554,0.000008053639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002722761,0.000114523704,0.0000059404697,0.00011196056,0.0000056330587,0.00008907912,0.0026198523,0.12932265,0.0011985245,0.0010210294,0.00004078546,0.8654428],"study_design_scores_gemma":[0.00025481227,0.00030716846,0.000097476935,0.0004139074,0.0000040017244,0.000033236636,4.371889e-7,0.89656836,0.009742605,0.09171278,0.0003240708,0.0005411583],"about_ca_topic_score_codex":0.000021090327,"about_ca_topic_score_gemma":0.000005462107,"teacher_disagreement_score":0.86490166,"about_ca_system_score_codex":0.00031927816,"about_ca_system_score_gemma":0.0005434347,"threshold_uncertainty_score":0.9996769},"labels":[],"label_agreement":null},{"id":"W3091086757","doi":"10.1109/iscas45731.2020.9181230","title":"PHMNet: A Deep Super Resolution Network using Parallel and Hierarchical Multi-Scale Residual Blocks","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Residual; Block (permutation group theory); Computer science; Abstraction; Artificial intelligence; Image (mathematics); Set (abstract data type); Scale (ratio); Pattern recognition (psychology); Scheme (mathematics); Deep learning; Algorithm; Mathematics","score_opus":0.04072073289056711,"score_gpt":0.2861463054904447,"score_spread":0.24542557259987757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091086757","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020694043,0.00056742015,0.9933334,0.002808734,0.00003364685,0.00014259559,4.906598e-7,0.00071452715,0.0003297915],"genre_scores_gemma":[0.061792653,0.000023366347,0.93621606,0.0017373485,0.00015621628,0.000007710535,0.0000011269917,0.000013458514,0.000052066058],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998626,0.00006981463,0.0002167655,0.00052376994,0.00019606974,0.00036760521],"domain_scores_gemma":[0.99939036,0.0000542289,0.00004929886,0.00027062403,0.00005683641,0.00017866721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016223773,0.000153524,0.0001695515,0.000035063702,0.00022023635,0.00015907957,0.00044452952,0.00008179516,0.000008505678],"category_scores_gemma":[0.000066957036,0.00014079011,0.00002809559,0.00031955997,0.00012846898,0.0006072682,0.0005951233,0.00024625444,0.0000038237513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009105119,0.0010960773,0.036097202,0.0005325008,0.00022944009,0.0006357912,0.03011138,0.08978144,0.2642554,0.077377416,0.036774926,0.4621979],"study_design_scores_gemma":[0.00028868817,0.00006668779,0.00038375874,0.000020871164,0.0000049738424,0.000027331724,0.000013212643,0.9923523,0.00068229163,0.004866582,0.0010959492,0.00019734485],"about_ca_topic_score_codex":0.000016905182,"about_ca_topic_score_gemma":0.000014392403,"teacher_disagreement_score":0.90257084,"about_ca_system_score_codex":0.000018606373,"about_ca_system_score_gemma":0.00004384795,"threshold_uncertainty_score":0.5741254},"labels":[],"label_agreement":null},{"id":"W3091128821","doi":"10.1109/iscas45731.2020.9180822","title":"EFFRBNet: A Deep Super Resolution Network using Edge-Assisted Feature Fusion Residual Blocks","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Residual; Enhanced Data Rates for GSM Evolution; Fusion; Feature (linguistics); Computer science; Artificial intelligence; Resolution (logic); Pattern recognition (psychology); Algorithm","score_opus":0.029424304531337955,"score_gpt":0.27188232571792326,"score_spread":0.2424580211865853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091128821","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015510507,0.00082103617,0.98930764,0.0051325746,0.00014036897,0.00019400379,6.569075e-7,0.0015049337,0.0013477253],"genre_scores_gemma":[0.05444991,0.000014746056,0.94238263,0.0025236583,0.00039327136,0.000007812523,0.0000046544087,0.000022316519,0.00020098926],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982552,0.00009071022,0.00023115255,0.0006263586,0.00034265235,0.00045390485],"domain_scores_gemma":[0.99903905,0.000053912194,0.00010320327,0.0004795852,0.00016086388,0.00016336243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020268184,0.0002170533,0.00021172683,0.00005894031,0.00034200074,0.0002304784,0.0008103052,0.00015837322,0.000021396956],"category_scores_gemma":[0.00012139671,0.00019511253,0.000058451646,0.0009458325,0.00006286729,0.00080266886,0.00071428233,0.00033810508,0.000012820939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003326926,0.00027116275,0.0023650252,0.00023732557,0.00008924263,0.00035051443,0.004033175,0.010805325,0.39516163,0.013326014,0.25235432,0.32067358],"study_design_scores_gemma":[0.00032708823,0.00013034802,0.00066064915,0.00009648314,0.000014975965,0.00006242211,0.000021622547,0.9766665,0.008498599,0.0040351716,0.009081188,0.00040492142],"about_ca_topic_score_codex":0.000014594487,"about_ca_topic_score_gemma":0.000010880659,"teacher_disagreement_score":0.9658612,"about_ca_system_score_codex":0.00008052463,"about_ca_system_score_gemma":0.00008655039,"threshold_uncertainty_score":0.7956458},"labels":[],"label_agreement":null},{"id":"W3092426380","doi":"10.1007/978-3-030-59830-3_57","title":"Single Image Super-Resolution for Medical Image Applications","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"MNIST database; Computer science; Discriminator; Artificial intelligence; Image (mathematics); Computer vision; Feature (linguistics); Pixel; Image resolution; Resolution (logic); Feature extraction; Superresolution; Mean squared error; Pattern recognition (psychology); High resolution; Deep learning; Telecommunications; Mathematics","score_opus":0.017470334031618406,"score_gpt":0.284122529824334,"score_spread":0.2666521957927156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092426380","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.020314e-7,0.0003995002,0.9895306,0.0058309,0.00037329266,0.00095123914,0.000015236303,0.0008341254,0.0020647089],"genre_scores_gemma":[0.0015033168,0.000026511452,0.9951922,0.0023839683,0.0005827705,0.0001432297,0.000017374388,0.00005329494,0.00009729481],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956309,0.000023948432,0.0006035371,0.001886039,0.001199578,0.0006560039],"domain_scores_gemma":[0.9970395,0.00053778023,0.00028286263,0.0012973024,0.00053231,0.00031021898],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00071789423,0.00051359634,0.0005101619,0.00048532127,0.0003756152,0.00071351876,0.004773598,0.0003657652,0.00001660866],"category_scores_gemma":[0.0005097062,0.0005059547,0.00015697573,0.00067213434,0.001283892,0.0013001916,0.0017941407,0.0008162782,0.000042459484],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000072897137,0.000065552,0.0000013026723,0.00013850213,0.000007033132,0.000046109122,0.00022475007,0.00012687888,0.010083543,0.03488712,0.00027668173,0.95413524],"study_design_scores_gemma":[0.0001962078,0.00016556603,0.0000016793573,0.00023658524,0.000007101293,0.00006169059,8.4548766e-8,0.5508388,0.009705001,0.42543054,0.012811235,0.0005454651],"about_ca_topic_score_codex":0.0000040448044,"about_ca_topic_score_gemma":0.000012378678,"teacher_disagreement_score":0.9535898,"about_ca_system_score_codex":0.00037597472,"about_ca_system_score_gemma":0.000833901,"threshold_uncertainty_score":0.9997392},"labels":[],"label_agreement":null},{"id":"W3097430696","doi":"10.1007/s00371-020-01998-z","title":"Joint restoration convolutional neural network for low-quality image super resolution","year":2020,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Compression artifact; Convolutional neural network; Artificial intelligence; Ringing artifacts; Residual; Benchmark (surveying); Concatenation (mathematics); Image (mathematics); Block (permutation group theory); Image restoration; Image quality; Feature (linguistics); Image compression; Pattern recognition (psychology); Interpolation (computer graphics); Computer vision; Image processing; Algorithm; Mathematics","score_opus":0.06393398593342653,"score_gpt":0.3432241822792017,"score_spread":0.2792901963457752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097430696","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054266327,0.000103663566,0.98351765,0.009539622,0.00031889888,0.00039291588,0.0000048191246,0.0006646213,0.000031166404],"genre_scores_gemma":[0.21497889,0.0000027781994,0.7783251,0.00532137,0.0012712339,0.000045735906,0.000020939231,0.000016652626,0.000017311017],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983673,0.00021631322,0.00036209915,0.00043297856,0.00028583573,0.0003354713],"domain_scores_gemma":[0.99899465,0.00017371592,0.00015716952,0.00032716306,0.00026056633,0.000086741566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006070746,0.00016957635,0.00019063428,0.000028361073,0.00037955612,0.0002516469,0.00066467584,0.000055835273,0.0000041422054],"category_scores_gemma":[0.000090507616,0.00013232985,0.00009608498,0.00031806098,0.00013394766,0.00089880504,0.00045427098,0.00020149077,0.000024558161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078259583,0.00046616484,0.0004178058,0.00049509393,0.000116966796,0.000023190576,0.0046335924,0.031583916,0.13035403,0.3370843,0.39715227,0.096890084],"study_design_scores_gemma":[0.00026421915,0.00023761882,0.0014103503,0.000019961715,0.0000045926777,0.00000556703,0.000003405322,0.9793056,0.0016132874,0.014730936,0.0022182458,0.0001861666],"about_ca_topic_score_codex":0.0000061108317,"about_ca_topic_score_gemma":0.0000010827152,"teacher_disagreement_score":0.9477217,"about_ca_system_score_codex":0.00006254775,"about_ca_system_score_gemma":0.00006609855,"threshold_uncertainty_score":0.53962547},"labels":[],"label_agreement":null},{"id":"W3101196487","doi":"10.1051/0004-6361/201219489/pdf","title":"Extended-object reconstruction in adaptive-optics imaging: the multiresolution approach","year":2013,"lang":"en","type":"article","venue":"Springer Link (Chiba Institute of Technology)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Office National d'études et de Recherches Aérospatiales; U.S. Air Force; Ministerio de Ciencia y Tecnología; Universitat Autònoma de Barcelona; University of Toronto","keywords":"Deconvolution; Artificial intelligence; Blind deconvolution; Computer vision; Iterative reconstruction; Wavelet; Computer science; Algorithm; Curvelet; Wavelet transform; Optics; Physics","score_opus":0.015294080372302859,"score_gpt":0.24233061302825135,"score_spread":0.22703653265594848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101196487","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024804821,0.0005784104,0.9665579,0.0027223518,0.00046466864,0.0006523148,8.30924e-7,0.001303995,0.0029147076],"genre_scores_gemma":[0.42116833,0.0000526226,0.57856345,0.00003908445,0.000033980843,0.00011221421,5.816196e-7,0.0000113733295,0.000018358174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982409,0.000028818951,0.0004983617,0.0005901264,0.00022038801,0.0004214455],"domain_scores_gemma":[0.9982932,0.000021585947,0.00032023794,0.0010996371,0.00022592997,0.000039409468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003156886,0.0002493785,0.0002792467,0.00070815335,0.00017789181,0.000085448344,0.0016400971,0.00020921486,0.0000018986135],"category_scores_gemma":[0.00024237615,0.00020370596,0.00007310962,0.0012856505,0.00091667526,0.0017599235,0.0007076075,0.00069036527,0.000016266948],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005974772,0.000121986035,0.0027354693,0.000032253214,0.000016516253,0.000005476746,0.00011661101,0.00019807056,0.020091588,0.11978888,0.000038394664,0.8568488],"study_design_scores_gemma":[0.00077045895,0.00012709359,0.010702448,0.00032971884,0.000017828694,0.00026637886,0.00014934769,0.73192245,0.060279988,0.19234721,0.0024157788,0.0006712716],"about_ca_topic_score_codex":0.00007488777,"about_ca_topic_score_gemma":0.0000071862783,"teacher_disagreement_score":0.8561775,"about_ca_system_score_codex":0.00013231789,"about_ca_system_score_gemma":0.000114954586,"threshold_uncertainty_score":0.83068883},"labels":[],"label_agreement":null},{"id":"W3107066575","doi":"10.1007/978-3-030-58520-4_27","title":"Conditional Entropy Coding for Efficient Video Compression","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Entropy encoding; Codec; Decoding methods; Conditional entropy; Data compression; Artificial intelligence; Autoregressive model; Deep learning; Entropy (arrow of time); Algorithm; Image compression; Theoretical computer science; Speech recognition; Computer vision; Computer engineering; Image processing; Principle of maximum entropy; Image (mathematics); Computer hardware","score_opus":0.020204340808646998,"score_gpt":0.2805028317985322,"score_spread":0.2602984909898852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107066575","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000028738293,0.0003237506,0.9952061,0.0017158891,0.00088457606,0.0006968253,0.000023444692,0.000585866,0.0005606492],"genre_scores_gemma":[0.046471342,0.000013763985,0.9507409,0.002196962,0.00040779557,0.000039685765,0.000022738597,0.000041499672,0.00006531112],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960297,0.000023942375,0.00055039366,0.0017924515,0.0009909401,0.00061257964],"domain_scores_gemma":[0.997342,0.00073294586,0.00041964615,0.00089327537,0.0004039042,0.00020823922],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004909619,0.0005357037,0.0005544255,0.00053612754,0.00044483264,0.00058729894,0.0033238211,0.0002367136,0.0000111233],"category_scores_gemma":[0.0002516258,0.000503745,0.00015917512,0.0004309034,0.0007032607,0.00054809405,0.0016172816,0.0006916919,0.000020426227],"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":[0.000043920674,0.00008829886,0.0000117693935,0.00029637027,0.000021813588,0.00012118766,0.0007257967,0.06309406,0.015707964,0.40374592,0.0004996847,0.51564324],"study_design_scores_gemma":[0.00022170396,0.00011092477,0.000005226902,0.00038302314,0.0000046955897,0.000027732563,4.563409e-8,0.6481101,0.009629805,0.33898896,0.0021155186,0.00040223566],"about_ca_topic_score_codex":0.000001406078,"about_ca_topic_score_gemma":0.0000011791197,"teacher_disagreement_score":0.5850161,"about_ca_system_score_codex":0.00036430708,"about_ca_system_score_gemma":0.000490819,"threshold_uncertainty_score":0.99974144},"labels":[],"label_agreement":null},{"id":"W3110278502","doi":"10.48550/arxiv.2011.01926","title":"Generating Unobserved Alternatives","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Econometrics; Economics; Computer science","score_opus":0.14140056309239676,"score_gpt":0.22514899124613727,"score_spread":0.0837484281537405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110278502","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02346446,0.000132786,0.9729933,0.00038513116,0.0002777196,0.00019530443,0.000007741202,0.0014988816,0.0010446628],"genre_scores_gemma":[0.63540983,0.000081848135,0.36376694,0.0002632344,0.000090199,0.0000012458935,0.0000075560165,0.00002073816,0.00035844572],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786884,0.00012570387,0.00020824347,0.0013776484,0.00010069853,0.0003188891],"domain_scores_gemma":[0.99816006,0.00006769352,0.0003523685,0.001067915,0.00019137893,0.00016057337],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001377643,0.00034678448,0.00033242156,0.00014574552,0.00017740451,0.00026641422,0.003065506,0.00017162271,0.000008609479],"category_scores_gemma":[0.00010559064,0.00041624776,0.00015591974,0.000512644,0.00010550303,0.0008068744,0.004517793,0.00071008445,0.00003875949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005361687,0.0002513245,0.0021854446,0.0006384856,0.00034912655,0.0031085392,0.001798122,0.3496296,0.023308054,0.60474426,0.0018380941,0.012095352],"study_design_scores_gemma":[0.00014046747,0.000028102422,0.000060408787,0.000088837725,0.000015631293,0.0000025789518,0.000015738124,0.81465423,0.0026656734,0.18166134,0.000280675,0.00038632084],"about_ca_topic_score_codex":0.00003449577,"about_ca_topic_score_gemma":0.0000049336604,"teacher_disagreement_score":0.61194533,"about_ca_system_score_codex":0.00016900203,"about_ca_system_score_gemma":0.00019646957,"threshold_uncertainty_score":0.99982893},"labels":[],"label_agreement":null},{"id":"W3117074306","doi":"10.1109/tnnls.2020.3045082","title":"Stabilizing Training of Generative Adversarial Nets via Langevin Stein Variational Gradient Descent","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Image Processing Techniques","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":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Gradient descent; Generative grammar; Adversarial system; Langevin dynamics; Artificial intelligence; Training (meteorology); Computer science; Descent (aeronautics); Stochastic gradient descent; Mathematics; Applied mathematics; Statistical physics; Artificial neural network; Physics","score_opus":0.03231864023372372,"score_gpt":0.24355968427013341,"score_spread":0.2112410440364097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117074306","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045736986,0.00029961634,0.9937351,0.0004061083,0.0004755029,0.00022407799,0.000002089047,0.00026178572,0.000022042765],"genre_scores_gemma":[0.9621374,0.000021146421,0.03750162,0.0001393613,0.0001389551,0.000025409605,0.000001691107,0.000015984862,0.000018403452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855065,0.00029202263,0.00032474115,0.00037452046,0.00022682341,0.00023124537],"domain_scores_gemma":[0.99929184,0.00018718716,0.00019651026,0.0001271254,0.000082097315,0.00011525048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024073613,0.00016933345,0.0002575641,0.00006421576,0.0002951505,0.000114295435,0.00020945394,0.00006878801,0.0000028808402],"category_scores_gemma":[0.000015168106,0.00015953205,0.00006034882,0.00028896102,0.00004977476,0.00036738996,0.0000059727863,0.00049559533,5.171281e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031501953,0.000023995231,0.000024553607,0.00003309324,0.000022611452,0.0000050592894,0.0025134278,0.9538489,0.0037378308,0.0001934692,0.000016580045,0.039548997],"study_design_scores_gemma":[0.0002837799,0.00032326297,0.000034568067,0.00007592719,0.0000118007065,0.000015672234,0.00016740241,0.99830055,0.00038897563,0.000022967908,0.00022248078,0.00015260394],"about_ca_topic_score_codex":0.00003181923,"about_ca_topic_score_gemma":0.000003148018,"teacher_disagreement_score":0.95756376,"about_ca_system_score_codex":0.000038217397,"about_ca_system_score_gemma":0.000028326393,"threshold_uncertainty_score":0.6505528},"labels":[],"label_agreement":null},{"id":"W3118836296","doi":"10.1109/wacv48630.2021.00103","title":"AutoRetouch: Automatic Professional Face Retouching","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Quest University Canada","funders":"","keywords":"Face (sociological concept); Computer science; Smoothing; Artificial intelligence; Computer vision; Photography; Facial recognition system; Texture (cosmology); Computer graphics (images); Pattern recognition (psychology); Image (mathematics); Visual arts; Art","score_opus":0.014875211483012442,"score_gpt":0.29904006438473124,"score_spread":0.2841648529017188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118836296","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016043707,0.00015784563,0.98418576,0.00391251,0.00029287493,0.000074127594,3.4989282e-7,0.0023427764,0.0074293837],"genre_scores_gemma":[0.0888955,0.0000039761794,0.9045851,0.0012832724,0.000029621064,0.000017130391,0.0000017551657,0.0000104457595,0.005173205],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869835,0.000076551965,0.00023217838,0.00040774237,0.00032644745,0.00025872776],"domain_scores_gemma":[0.9989826,0.000099682715,0.000084586594,0.0006137162,0.0001450815,0.00007431578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020209509,0.00012749883,0.00014601689,0.00005404729,0.00017674595,0.00020130884,0.00070607156,0.000054720596,0.00008886355],"category_scores_gemma":[0.00028302104,0.00010837934,0.00004303483,0.00044806756,0.00003492565,0.001083406,0.0006046057,0.00019094882,0.00005583339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002235775,0.00029349566,0.00062011706,0.00021067457,0.00003073598,0.00036741194,0.002978251,0.000023368124,0.12657863,0.35344112,0.035470784,0.47998318],"study_design_scores_gemma":[0.00020295312,0.000027879309,0.0008332038,0.00022662127,0.000004361465,0.00018842577,0.000151168,0.66409504,0.17881726,0.14772668,0.007278218,0.00044820915],"about_ca_topic_score_codex":0.0000015239813,"about_ca_topic_score_gemma":9.671994e-7,"teacher_disagreement_score":0.6640717,"about_ca_system_score_codex":0.000055323042,"about_ca_system_score_gemma":0.00028296848,"threshold_uncertainty_score":0.44195813},"labels":[],"label_agreement":null},{"id":"W3121479933","doi":"10.2139/ssrn.2840552","title":"Super-resolution Estimation of Cyclic Arrival Rates","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Hong Kong University of Science and Technology; National Science Foundation","keywords":"Estimation; Statistics; Econometrics; Geodesy; Computer science; Mathematics; Geography; Economics","score_opus":0.007816771840659398,"score_gpt":0.27184356546626726,"score_spread":0.2640267936256079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121479933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019376121,0.0009930353,0.9779813,0.001286973,0.00007531517,0.0000526283,3.9318365e-7,0.00013850951,0.00009567258],"genre_scores_gemma":[0.82692635,0.0007025496,0.17211983,0.000023244747,0.000048110152,0.0000034128782,2.6847164e-7,0.000008808456,0.00016744243],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99827594,0.00006133195,0.00026373862,0.00017855596,0.0002386043,0.000981861],"domain_scores_gemma":[0.99931,0.00006988892,0.00020702486,0.00023990683,0.00012951219,0.000043649616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087413035,0.000104829756,0.00012544212,0.00013469002,0.00011645982,0.000046459958,0.00063128927,0.000043574484,0.0000035404491],"category_scores_gemma":[0.00023214438,0.00007192879,0.00005641962,0.00021404045,0.00006643147,0.0013237501,0.00008024205,0.00037666317,0.000011462191],"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":[0.00001588059,0.00004404462,0.00027874424,0.00000633375,0.000022704275,0.000001516903,0.0000906302,0.000059454793,0.09749143,0.29587862,0.000061282924,0.60604936],"study_design_scores_gemma":[0.000309384,0.00022483124,0.00018016201,0.00006936315,0.000005699405,0.00029211032,0.000020860181,0.016602129,0.046133224,0.9358979,0.00014020744,0.0001241643],"about_ca_topic_score_codex":0.0000054127004,"about_ca_topic_score_gemma":0.000009505811,"teacher_disagreement_score":0.8075502,"about_ca_system_score_codex":0.0004601524,"about_ca_system_score_gemma":0.00079825055,"threshold_uncertainty_score":0.2933171},"labels":[],"label_agreement":null},{"id":"W3123507393","doi":"10.15353/jcvis.v6i1.3555","title":"Temporally Consistent Edge-Informed Video Super-Resolution (Edge-VSR)","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Superresolution; Frame (networking); Computer vision; Convolutional neural network; Optical flow; Resolution (logic); Low resolution; Pattern recognition (psychology); Image (mathematics); High resolution; Remote sensing; Telecommunications; Geography","score_opus":0.014505128671240897,"score_gpt":0.30297975714935815,"score_spread":0.2884746284781173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123507393","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002875176,0.005531723,0.98641866,0.0037217462,0.0008049866,0.000097273165,0.000001797904,0.00009612396,0.0004525384],"genre_scores_gemma":[0.58683705,0.00007386892,0.41233563,0.0004436478,0.00014729619,0.0000021841954,0.000004286122,0.000010281143,0.00014572585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997961,0.00016698371,0.0007828379,0.00019946192,0.00068306335,0.00020666448],"domain_scores_gemma":[0.99688494,0.00031873022,0.0005588707,0.0001831226,0.0018853041,0.00016905999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007673823,0.00015490387,0.00031165103,0.00027124493,0.00025955928,0.00080470974,0.00034028664,0.000041267296,0.0000036594224],"category_scores_gemma":[0.00041786523,0.00013595757,0.00009987024,0.00029617848,0.00010296852,0.0019307063,0.00016719034,0.00021257647,0.0000037522636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030212395,0.0014862973,0.014696136,0.0021567277,0.00050920824,0.0027682458,0.00875309,0.05292152,0.04562061,0.2782649,0.15777786,0.43474326],"study_design_scores_gemma":[0.0016241176,0.00019000584,0.0057792002,0.0017720019,0.000028036851,0.0062526786,0.0004874146,0.8497597,0.0004904691,0.029979592,0.10317605,0.00046070508],"about_ca_topic_score_codex":0.000005610972,"about_ca_topic_score_gemma":5.6770944e-7,"teacher_disagreement_score":0.7968382,"about_ca_system_score_codex":0.000149645,"about_ca_system_score_gemma":0.0008957889,"threshold_uncertainty_score":0.77598345},"labels":[],"label_agreement":null},{"id":"W3124521503","doi":"10.20944/preprints202003.0313.v3","title":"Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network","year":2020,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Energy; Athabasca University; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Detector; Computer science; Residual; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Context (archaeology); Computer vision; Overhead (engineering); Generative adversarial network; Object detection; Image resolution; Remote sensing; Deep learning; Pattern recognition (psychology); Telecommunications; Algorithm; Geography","score_opus":0.05712182583916333,"score_gpt":0.3082877557819736,"score_spread":0.2511659299428103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124521503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1716924,0.00023154498,0.823715,0.00041240428,0.00031688545,0.0010743487,0.000003225981,0.001496422,0.0010578178],"genre_scores_gemma":[0.62397,0.00008780181,0.37539998,0.00022654692,0.00015817603,0.000027487626,0.0000027710444,0.00007290278,0.00005435321],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9946263,0.00037702778,0.00074991665,0.0028467032,0.0005062278,0.0008938157],"domain_scores_gemma":[0.99656296,0.00023129887,0.00058524543,0.002040605,0.00030220358,0.00027771134],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010233095,0.00081048487,0.0009282608,0.00042928947,0.0002605484,0.0003143884,0.0014938024,0.0003732877,0.000010726193],"category_scores_gemma":[0.0007290267,0.00084520684,0.00012695398,0.0011490239,0.00019439013,0.0005112548,0.004586654,0.0019328338,0.0000550093],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031100822,0.00004147763,0.003447966,0.0006007467,0.00010895302,0.0002494792,0.0031028956,0.0036666896,0.40769765,0.000021637265,0.000009765554,0.5807417],"study_design_scores_gemma":[0.00043554936,0.00014362128,0.029550884,0.001655224,0.000042137508,0.0001009544,0.00003337047,0.055944752,0.8962991,0.0143862665,0.00020868072,0.0011994614],"about_ca_topic_score_codex":0.00040118484,"about_ca_topic_score_gemma":0.00037941724,"teacher_disagreement_score":0.5795423,"about_ca_system_score_codex":0.0003811974,"about_ca_system_score_gemma":0.00034307965,"threshold_uncertainty_score":0.9993999},"labels":[],"label_agreement":null},{"id":"W3133206434","doi":"10.1146/annurev-vision-093019-115521","title":"Mobile Computational Photography: A Tour","year":2021,"lang":"en","type":"preprint","venue":"Annual Review of Vision Science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Photography; Computer science; Parallels; Mobile phone; Computational photography; Camera phone; Multimedia; Computer graphics (images); Computer vision; Key (lock); Artificial intelligence; Image processing; Visual arts; Telecommunications; Art; Image (mathematics); Engineering; Computer security","score_opus":0.01573272124592928,"score_gpt":0.39044079336471443,"score_spread":0.37470807211878515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133206434","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026436846,0.09712401,0.9003055,0.00022922589,0.00041984304,0.0005622832,0.000021557958,0.00022208621,0.00085110683],"genre_scores_gemma":[0.031720378,0.02586193,0.94054323,0.0016858886,0.000030013465,0.0001112693,0.000012892347,0.000011421728,0.000022946264],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99582946,0.00010437461,0.0007709613,0.0012348002,0.0017175612,0.00034281358],"domain_scores_gemma":[0.994721,0.00009369467,0.0007613866,0.0014902531,0.0027767366,0.00015695648],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0021242015,0.0002860743,0.0006485245,0.0003673003,0.0001776589,0.00031976105,0.0043537184,0.00008339926,0.000028740647],"category_scores_gemma":[0.0007264716,0.0002517331,0.00026350952,0.0024873402,0.00071360834,0.0017416697,0.005379129,0.00043802225,0.000007983196],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041275716,0.000479459,0.00004859821,0.01826067,0.000023055136,0.000058142992,0.0021162909,0.00048814213,0.0056264484,0.0040944484,0.010225596,0.958575],"study_design_scores_gemma":[0.0008371127,0.0022802022,0.0026864265,0.3704529,0.00016797232,0.00043396218,0.0006074669,0.29850665,0.03461347,0.18213111,0.102077186,0.005205539],"about_ca_topic_score_codex":0.000011241662,"about_ca_topic_score_gemma":3.5322225e-7,"teacher_disagreement_score":0.9533695,"about_ca_system_score_codex":0.000064889166,"about_ca_system_score_gemma":0.0013660722,"threshold_uncertainty_score":0.9999935},"labels":[],"label_agreement":null},{"id":"W3135420168","doi":"10.1007/978-3-030-67070-2_1","title":"AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results","year":2020,"lang":"en","type":"book-chapter","venue":"Institutional Research Information System (University of Udine)","topic":"Advanced Image Processing 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":"Centre for Social Innovation; McMaster University","funders":"","keywords":"Computer science; Focus (optics); FLOPS; Image (mathematics); Set (abstract data type); Resolution (logic); Magnification; Artificial intelligence; Factor (programming language); State (computer science); Computer engineering; Algorithm; Parallel computing; Programming language","score_opus":0.0992968842218422,"score_gpt":0.3435963320656298,"score_spread":0.2442994478437876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135420168","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000002865838,0.00015770305,0.6401932,0.0027995124,0.000091785754,0.00034171905,0.00010545348,0.00020386366,0.35610387],"genre_scores_gemma":[0.0622233,0.0005858713,0.9229909,0.00013219107,0.00020223371,0.000006488735,0.0002062984,0.000024244158,0.013628455],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99722093,0.00013194855,0.0004931878,0.00043045953,0.0014625347,0.00026095813],"domain_scores_gemma":[0.9972201,0.00027344763,0.00041029917,0.0005207227,0.0013538018,0.00022160969],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020344076,0.00022936886,0.00036343053,0.00072269794,0.0007362423,0.00011436656,0.0010756903,0.00025809722,0.000012242525],"category_scores_gemma":[0.00040640667,0.00025783211,0.00010004288,0.0002942919,0.0006189986,0.0015020747,0.0010389107,0.000690578,0.00021972129],"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":[0.00013815473,0.000012843286,1.1164425e-7,0.0004785805,0.000029543293,0.000038490383,0.0008978664,0.00044492964,0.000016515283,0.9761836,0.003056889,0.018702455],"study_design_scores_gemma":[0.0010155415,0.000492552,0.000019773182,0.001793866,0.000013074895,0.000080131606,0.0004218283,0.29507425,0.000056346576,0.005732225,0.69488364,0.0004167567],"about_ca_topic_score_codex":0.000028313518,"about_ca_topic_score_gemma":0.0000023894106,"teacher_disagreement_score":0.9704514,"about_ca_system_score_codex":0.00064612064,"about_ca_system_score_gemma":0.00064238836,"threshold_uncertainty_score":0.99998736},"labels":[],"label_agreement":null},{"id":"W3137806071","doi":"10.1109/bmsb49480.2020.9379855","title":"Artifacts Reduction GAN For Enhancing Quality Of Compressed Panoramic Video","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Coding (social sciences); Video quality; Generative adversarial network; Convolutional neural network; Visualization; Perception; Deep learning","score_opus":0.07153877255478729,"score_gpt":0.34509671533017905,"score_spread":0.27355794277539175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137806071","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055791773,0.000032178406,0.99154407,0.0016315184,0.000056438705,0.0002039241,0.0000010895473,0.00062272616,0.00032886898],"genre_scores_gemma":[0.53114057,0.0000013967397,0.46861887,0.00018623288,0.000019380677,0.000009499279,9.56397e-7,0.000004259071,0.000018832463],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99903995,0.000037193488,0.00032378297,0.00030406137,0.00014394787,0.00015106078],"domain_scores_gemma":[0.99920774,0.00008747931,0.0001896566,0.00028306808,0.00017220534,0.000059872455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020966238,0.00008725085,0.00017944918,0.000031839754,0.00005627648,0.00005277483,0.00043206642,0.000032738782,0.0000044181766],"category_scores_gemma":[0.00027616127,0.00008329714,0.000047207857,0.0002279726,0.00004086806,0.000661631,0.00009629688,0.00006338366,0.000003530358],"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":[0.000013422551,0.000019972182,0.000015285026,0.00010383006,0.0000034851835,1.386518e-7,0.0003989487,0.000052962783,0.98034626,0.0052558524,0.00026055082,0.013529316],"study_design_scores_gemma":[0.00013366618,0.000072689945,0.000080751175,0.000019191604,0.0000020335578,9.735944e-7,0.00003591354,0.071244076,0.90920025,0.018850822,0.0002577688,0.00010184478],"about_ca_topic_score_codex":0.000014321023,"about_ca_topic_score_gemma":0.0000032625064,"teacher_disagreement_score":0.5255614,"about_ca_system_score_codex":0.00001945524,"about_ca_system_score_gemma":0.000047168072,"threshold_uncertainty_score":0.33967587},"labels":[],"label_agreement":null},{"id":"W3139549852","doi":"10.1145/3448104","title":"SplitSR","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Residual; Mobile device; Inference; Latency (audio); Deep learning; Computer engineering; Cloud computing; Bilinear interpolation; Convolutional neural network; Artificial intelligence; Ranging; Computation; Computer vision; Algorithm; Operating system","score_opus":0.010492240781202433,"score_gpt":0.2649735983193623,"score_spread":0.25448135753815987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139549852","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9465475,0.0051561045,0.011117968,0.014146434,0.0005339785,0.0009773049,0.000008341008,0.0048528546,0.016659504],"genre_scores_gemma":[0.8604812,0.00049095857,0.13822924,0.00016093536,0.000013744114,0.0001608405,1.1405392e-7,0.000014939576,0.00044803385],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987246,0.0000072821085,0.00023400052,0.00052667886,0.00023117989,0.0002763066],"domain_scores_gemma":[0.99820024,0.0001529572,0.00026545086,0.00087086385,0.0004889093,0.000021565373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014227546,0.0001981937,0.00026042605,0.00013435846,0.00018531343,0.00018128272,0.0029668855,0.000116540396,0.0000029206374],"category_scores_gemma":[0.0022732783,0.0001416982,0.00007577051,0.00063792785,0.00026696516,0.0008908999,0.0045682183,0.000431323,0.0000040951977],"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":[0.000039701918,0.00021825534,0.0011648104,0.00016024147,0.000049547336,0.000006993096,0.0005261974,0.0000031036534,0.7349803,0.03540719,0.0026693197,0.22477435],"study_design_scores_gemma":[0.00008611124,0.00017832727,0.00016321462,0.0003192069,0.0000060697876,0.00005604542,0.0009787463,0.0002390455,0.78734607,0.20840178,0.0020930842,0.00013229366],"about_ca_topic_score_codex":0.0000039447123,"about_ca_topic_score_gemma":5.469167e-7,"teacher_disagreement_score":0.22464205,"about_ca_system_score_codex":0.0000565457,"about_ca_system_score_gemma":0.000041888215,"threshold_uncertainty_score":0.57782847},"labels":[],"label_agreement":null},{"id":"W3140801471","doi":"10.48550/arxiv.2103.15368","title":"Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pixel; Computer science; Artificial intelligence; Computer vision; Compression (physics); Image compression; Layer (electronics); Sketch; Region of interest; Texture compression; Image (mathematics); Data compression; Pattern recognition (psychology); Image processing; Algorithm; Materials science","score_opus":0.03678798240684051,"score_gpt":0.21243979202780758,"score_spread":0.17565180962096708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3140801471","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15076461,0.0002124885,0.8468004,0.000053249387,0.00042619734,0.00028706045,0.000017349877,0.00049394486,0.0009447009],"genre_scores_gemma":[0.76300293,0.00025479027,0.2363342,0.000033373184,0.000025921832,0.0000020544717,0.00006675649,0.000024718987,0.00025523006],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972819,0.0002927897,0.00047487914,0.001404928,0.00018736915,0.0003581011],"domain_scores_gemma":[0.99642295,0.00011074767,0.0010316462,0.0014715361,0.0008217704,0.00014134632],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018484704,0.0004134999,0.00057708664,0.00029143246,0.00020666912,0.00017341213,0.0016669859,0.0003431756,0.000030278567],"category_scores_gemma":[0.00008251058,0.0004983159,0.00026578273,0.000674092,0.00033982017,0.0012529417,0.002362713,0.00066074793,0.000007294057],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014406312,0.0012383484,0.006448723,0.002126483,0.00048069566,0.0011395913,0.0013853532,0.024122082,0.90333855,0.018722596,0.0053162375,0.035537258],"study_design_scores_gemma":[0.00089550205,0.00007218045,0.0005773702,0.0012665509,0.00010564002,0.00007610631,0.00023924494,0.87057024,0.085495375,0.039643068,0.000095534575,0.00096316787],"about_ca_topic_score_codex":0.00011450979,"about_ca_topic_score_gemma":0.0000074011296,"teacher_disagreement_score":0.8464482,"about_ca_system_score_codex":0.00022446946,"about_ca_system_score_gemma":0.00019330722,"threshold_uncertainty_score":0.99974686},"labels":[],"label_agreement":null},{"id":"W3150396707","doi":"10.1109/tci.2021.3070522","title":"SRNSSI: A Deep Light-Weight Network for Single Image Super Resolution Using Spatial and Spectral Information","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Imaging","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Residual; Block (permutation group theory); Computer science; Feature (linguistics); Image resolution; Benchmark (surveying); Artificial intelligence; Pattern recognition (psychology); Feature extraction; Set (abstract data type); Computer vision; Algorithm; Mathematics","score_opus":0.013401995182387688,"score_gpt":0.2597127222508836,"score_spread":0.2463107270684959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3150396707","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00051916746,0.0001505994,0.9968416,0.0012888977,0.00044794678,0.00021770936,0.000012893678,0.00036607505,0.00015510146],"genre_scores_gemma":[0.3042913,0.000004201652,0.6951745,0.00038456422,0.00008821172,0.000022276405,0.000014485695,0.000013104638,0.0000073128235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986346,0.000055399825,0.0003484623,0.00035589424,0.00027539185,0.00033026692],"domain_scores_gemma":[0.99897456,0.0001728942,0.000119734534,0.00019781296,0.0004557058,0.00007926324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001607609,0.00019009846,0.0001630408,0.00018084256,0.0005789633,0.0005055772,0.00018193011,0.00004452826,0.000007522926],"category_scores_gemma":[0.000018610779,0.00021718099,0.00008139214,0.00040600452,0.000066501925,0.0032457397,0.00000958573,0.00018132545,0.0000050097688],"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":[0.00010024077,0.000394587,0.00009865754,0.0001725026,0.000085330044,0.000044307293,0.0017435226,0.6548205,0.06245971,0.0058027767,0.0005365938,0.27374125],"study_design_scores_gemma":[0.00044296775,0.00003593946,0.000085180625,0.000073942836,0.000020436182,0.000215064,0.000023896237,0.94772434,0.026167847,0.024622198,0.00035028442,0.00023791316],"about_ca_topic_score_codex":0.00001414272,"about_ca_topic_score_gemma":0.000009190061,"teacher_disagreement_score":0.30377215,"about_ca_system_score_codex":0.00017123127,"about_ca_system_score_gemma":0.00014757503,"threshold_uncertainty_score":0.88563836},"labels":[],"label_agreement":null},{"id":"W3151671170","doi":"10.1109/tbc.2021.3068862","title":"UPDResNN: A Deep Light-Weight Image Upsampling and Deblurring Residual Neural Network","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Broadcasting","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deblurring; Upsampling; Residual; Computer science; Artificial intelligence; Block (permutation group theory); Benchmark (surveying); Image restoration; Artificial neural network; Computer vision; Image (mathematics); Pattern recognition (psychology); Image processing; Algorithm; Mathematics","score_opus":0.017040157293292377,"score_gpt":0.2621407453221894,"score_spread":0.245100588028897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151671170","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036437518,0.0007341252,0.9930262,0.0005763106,0.00052618334,0.00011880951,0.000001655665,0.00093336543,0.0004396139],"genre_scores_gemma":[0.23659167,0.00004772547,0.7627507,0.00028834504,0.00016519481,0.000027703869,6.853297e-7,0.000033920056,0.00009410039],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795973,0.00009376834,0.00036117353,0.0007313166,0.00027984072,0.00057415094],"domain_scores_gemma":[0.99873567,0.0002120869,0.0001201092,0.00050875876,0.00027690976,0.00014647438],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023401163,0.00026989763,0.00024727173,0.00013217653,0.00081523927,0.000469451,0.0004038097,0.00008983006,0.000011899572],"category_scores_gemma":[0.000067048146,0.00029209058,0.000060605915,0.0010376251,0.0000642254,0.0012114745,0.000027005011,0.0005389838,0.00000827366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005231048,0.00022527181,0.0001188742,0.00018824678,0.000084686646,0.0006892896,0.0022640931,0.07892046,0.09894869,0.0009970957,0.00027555853,0.8172354],"study_design_scores_gemma":[0.00040022598,0.000077401724,0.000056970424,0.00032781795,0.000029569748,0.000603333,0.000071255294,0.85453355,0.14029975,0.0025309906,0.0005466401,0.00052250654],"about_ca_topic_score_codex":0.000008343669,"about_ca_topic_score_gemma":0.00001700601,"teacher_disagreement_score":0.8167129,"about_ca_system_score_codex":0.00006173618,"about_ca_system_score_gemma":0.000084871535,"threshold_uncertainty_score":0.99995315},"labels":[],"label_agreement":null},{"id":"W3155731499","doi":"10.48550/arxiv.2104.09752","title":"Flow-based Video Segmentation for Human Head and Shoulders","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Computer vision; Optical flow; Background subtraction; Videoconferencing; Convolutional neural network; Encoder; Process (computing); Computer graphics (images); Image (mathematics); Multimedia; Pixel","score_opus":0.09743156365197027,"score_gpt":0.2564366743295602,"score_spread":0.15900511067758993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155731499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035642024,0.000095178286,0.962952,0.0001526147,0.00013281895,0.00039524733,0.000008314364,0.00049794064,0.00012383501],"genre_scores_gemma":[0.5871692,0.000021613101,0.41242716,0.00017483953,0.000018956871,0.00000516594,0.000037621372,0.000014901008,0.00013058784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984335,0.00006260795,0.00015480083,0.001035938,0.00006989921,0.00024328622],"domain_scores_gemma":[0.9987077,0.00008565492,0.00018727391,0.0007051746,0.00021963287,0.00009453298],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016122084,0.0002466681,0.0002453719,0.00019629978,0.00024004877,0.00026307607,0.0007718839,0.000176643,0.0000049586415],"category_scores_gemma":[0.000037230788,0.00031540316,0.00011057697,0.00028039786,0.00010812317,0.0006074045,0.00083552557,0.00027259535,0.0000010228161],"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":[0.000311162,0.0012323986,0.008925299,0.0053990213,0.0005985999,0.0012916273,0.0038448304,0.62699157,0.06629659,0.18142824,0.0025180783,0.1011626],"study_design_scores_gemma":[0.00058760744,0.00006832389,0.00013459304,0.00019404586,0.00004497465,0.0000018880722,0.000063024796,0.92058516,0.008560598,0.069260515,0.000101837766,0.00039741158],"about_ca_topic_score_codex":0.000037905647,"about_ca_topic_score_gemma":0.00004760694,"teacher_disagreement_score":0.55152714,"about_ca_system_score_codex":0.00017745259,"about_ca_system_score_gemma":0.00017897908,"threshold_uncertainty_score":0.9999298},"labels":[],"label_agreement":null},{"id":"W3158098738","doi":"10.1109/iscas51556.2021.9401522","title":"MorphoNet: A Deep Image Super Resolution Network Using Hierarchical and Morphological Feature Generating Residual Blocks","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Residual; Computer science; Fuse (electrical); Artificial intelligence; Block (permutation group theory); Pattern recognition (psychology); Convolutional neural network; Feature (linguistics); Feature extraction; Computer vision; Algorithm; Mathematics; Engineering","score_opus":0.018330541525099754,"score_gpt":0.27296659939361767,"score_spread":0.2546360578685179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158098738","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031420574,0.0016272875,0.9639328,0.0019170573,0.00008540756,0.00009134436,0.0000010440381,0.00051766285,0.00040678272],"genre_scores_gemma":[0.026493385,0.000041113344,0.9717327,0.00115231,0.00029564247,0.000008913696,0.0000061693777,0.000015833239,0.00025393057],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809986,0.00017176484,0.00023023665,0.0007325681,0.00027381882,0.00049177586],"domain_scores_gemma":[0.99905854,0.000106341446,0.00006424265,0.0004712565,0.00017363929,0.00012595144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032696375,0.00020498486,0.00022529459,0.00005249402,0.00042088088,0.00047044278,0.00036615194,0.00017062157,0.0000302274],"category_scores_gemma":[0.00021836843,0.00017980208,0.000042635602,0.0005056493,0.00016992145,0.0007450852,0.0009382731,0.0005235879,0.0000021830638],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022486367,0.00008795409,0.000742961,0.000025839558,0.000020963278,0.0015503267,0.00035074027,0.0020724726,0.9600512,0.0068021496,0.009399582,0.01887333],"study_design_scores_gemma":[0.0002347281,0.000049786493,0.0003809308,0.000048064365,0.000009740902,0.0017255252,0.000022256032,0.9520512,0.025816057,0.018829493,0.0004925573,0.0003396541],"about_ca_topic_score_codex":0.000012030126,"about_ca_topic_score_gemma":0.000011855075,"teacher_disagreement_score":0.9499787,"about_ca_system_score_codex":0.000052225674,"about_ca_system_score_gemma":0.000105074265,"threshold_uncertainty_score":0.73321164},"labels":[],"label_agreement":null},{"id":"W3158196134","doi":"10.1109/iscas51556.2021.9401641","title":"MISNet: Multi-Resolution Level Feature Interpolating Ultralight-Weight Residual Image Super Resolution Network","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Residual; Benchmark (surveying); Feature (linguistics); Interpolation (computer graphics); Computer science; Convolutional neural network; Artificial intelligence; Image resolution; Pattern recognition (psychology); Resolution (logic); Image (mathematics); Computer vision; Algorithm; Geography; Cartography","score_opus":0.03340662461550179,"score_gpt":0.2917380005966697,"score_spread":0.2583313759811679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158196134","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021952533,0.0011889411,0.9906899,0.0037924757,0.0003332537,0.0001540251,0.0000061339983,0.0012000778,0.0024156896],"genre_scores_gemma":[0.012153984,0.00004314076,0.9821587,0.0007660449,0.00033188603,0.000021331725,0.00003006832,0.000031897438,0.0044629127],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99752825,0.00019616589,0.00037400256,0.0008398568,0.00037850646,0.00068319286],"domain_scores_gemma":[0.998358,0.00009388467,0.00014937077,0.0008350765,0.00043940503,0.00012424462],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004056392,0.00029701274,0.00025743115,0.00009567118,0.00042638747,0.00042331015,0.0008065402,0.00019528571,0.000036651065],"category_scores_gemma":[0.00031627528,0.0002772061,0.00009715231,0.00078666594,0.00010154151,0.0019845618,0.0005840023,0.00047178572,0.000037785623],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048304533,0.00036985593,0.0014390778,0.00013788167,0.00008644798,0.00036197595,0.002061548,0.0007752408,0.7117943,0.0381238,0.21696822,0.027833337],"study_design_scores_gemma":[0.0009052025,0.00009966371,0.005827317,0.00044223972,0.00002565558,0.0003089848,0.00009396228,0.7871625,0.179539,0.01198128,0.012550793,0.0010634462],"about_ca_topic_score_codex":0.000037002195,"about_ca_topic_score_gemma":0.0001015482,"teacher_disagreement_score":0.7863872,"about_ca_system_score_codex":0.00016568013,"about_ca_system_score_gemma":0.00015354886,"threshold_uncertainty_score":0.999968},"labels":[],"label_agreement":null},{"id":"W3158626905","doi":"10.1109/icpr48806.2021.9412463","title":"An Adaptive Model for Face Distortion Correction","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Distortion (music); Mobile device; Computer vision; Face (sociological concept); Artificial intelligence; Fidelity; High fidelity; Selfie; Computational photography; Photography; Computer graphics (images); Image (mathematics); Image processing; Telecommunications","score_opus":0.03238215593467712,"score_gpt":0.31392070474290173,"score_spread":0.2815385488082246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158626905","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000093428294,0.000029783758,0.9979914,0.00016128864,0.00016515699,0.00008139695,8.7798105e-7,0.00067678053,0.0007998875],"genre_scores_gemma":[0.30614477,0.0000019815993,0.6919566,0.00013633947,0.000011813587,0.00002829446,0.0000029993612,0.000004201326,0.0017130319],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946713,0.000011882519,0.00007886152,0.00026378018,0.00007708184,0.00010124259],"domain_scores_gemma":[0.99945945,0.000018055385,0.00003757524,0.00026161288,0.000190818,0.000032490247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006106457,0.000056892215,0.000055968416,0.000022611708,0.000088469395,0.00007251602,0.00019348906,0.000026783899,0.0000014596524],"category_scores_gemma":[0.000037721427,0.000056450524,0.000022528046,0.0001253141,0.000012222356,0.0011328382,0.000047722253,0.000041454867,0.0000018063473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031705655,0.00024418,0.000030157804,0.000016504117,0.000007884496,0.0000041979615,0.0015109151,0.02008696,0.07702502,0.067903794,0.006320513,0.82681817],"study_design_scores_gemma":[0.000044477765,0.000050002887,0.000011912593,0.000004784685,0.0000013155328,0.0000046575246,0.000027791231,0.88161325,0.07894858,0.039096646,0.00012414657,0.00007245597],"about_ca_topic_score_codex":0.0000025862796,"about_ca_topic_score_gemma":0.000011704937,"teacher_disagreement_score":0.86152625,"about_ca_system_score_codex":0.00005726263,"about_ca_system_score_gemma":0.00006775112,"threshold_uncertainty_score":0.23019856},"labels":[],"label_agreement":null},{"id":"W3163948607","doi":"10.1186/s13640-021-00552-8","title":"Single image super resolution based on multi-scale structure and non-local smoothing","year":2021,"lang":"en","type":"article","venue":"EURASIP Journal on Image and Video Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Ottawa","funders":"University of Electronic Science and Technology of China","keywords":"Bicubic interpolation; Artificial intelligence; Gaussian blur; Computer science; Interpolation (computer graphics); Smoothing; Gaussian; Pattern recognition (psychology); Gaussian function; Computer vision; Image (mathematics); Biometrics; Image resolution; Algorithm; Mathematics; Image restoration; Linear interpolation; Image processing","score_opus":0.01538352306831661,"score_gpt":0.28206039206250977,"score_spread":0.2666768689941932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163948607","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019486612,0.0010486138,0.97662264,0.0021378438,0.00012469418,0.00008584202,0.0000033512342,0.00019281438,0.00029760733],"genre_scores_gemma":[0.42665502,0.00003317119,0.5716925,0.0014447932,0.00009980849,0.0000022563522,0.0000018254798,0.000027931907,0.000042678814],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997837,0.00014123421,0.00041831742,0.0006776584,0.00045844947,0.0004673122],"domain_scores_gemma":[0.99860233,0.0001200339,0.00026345262,0.00033075374,0.000445026,0.0002383794],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00043436227,0.00033187593,0.0003120852,0.00023953372,0.0009029764,0.0021310134,0.00036413988,0.00011757069,0.000009409267],"category_scores_gemma":[0.00030898658,0.00029028513,0.00006362852,0.00041499833,0.00023051369,0.00302954,0.00019558056,0.0008831407,0.0000025797322],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068474124,0.00019541732,0.00039629766,0.00019429333,0.000008200874,0.00061890157,0.0009803773,0.00008603978,0.6369676,0.000023544917,0.0003081603,0.3601527],"study_design_scores_gemma":[0.0015581645,0.00039037495,0.0026584913,0.0017057761,0.000027085045,0.0013869255,0.0002101584,0.5781173,0.4093429,0.003179555,0.0007806235,0.00064262334],"about_ca_topic_score_codex":0.0000022646484,"about_ca_topic_score_gemma":0.0000028153952,"teacher_disagreement_score":0.57803124,"about_ca_system_score_codex":0.00011610867,"about_ca_system_score_gemma":0.00020639914,"threshold_uncertainty_score":0.99995494},"labels":[],"label_agreement":null},{"id":"W3171690480","doi":"10.3390/rs13122308","title":"Spatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Saskatchewan","funders":"","keywords":"Computer science; Artificial intelligence; Image resolution; Aerial image; Computer vision; Remote sensing; Image (mathematics); Geography","score_opus":0.02116092631474593,"score_gpt":0.28609478123203047,"score_spread":0.26493385491728455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171690480","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014376666,0.000084224084,0.9963174,0.0006293934,0.0002957819,0.00016725171,0.000010591738,0.00036532772,0.0006923703],"genre_scores_gemma":[0.093669266,0.00001194789,0.9059023,0.00012487103,0.00019872338,7.4777475e-8,0.000020428686,0.000023052236,0.00004932623],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985782,0.00008759086,0.00033923096,0.00044538052,0.00022301413,0.00032659806],"domain_scores_gemma":[0.9986125,0.00019462995,0.0001843719,0.0005169472,0.0004398781,0.000051673396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029634524,0.0001597611,0.00026314546,0.00014507465,0.00016505271,0.0001289858,0.00022834976,0.00005584286,0.0000016740785],"category_scores_gemma":[0.00035104953,0.00017333009,0.00009468673,0.0003539651,0.00008910892,0.0004037384,0.00015563105,0.00011975407,0.000001407163],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005100975,0.0000151188515,0.0000024318335,0.00010270946,0.000007429519,0.00004237887,0.00009803969,0.00009572736,0.77185994,0.00040331337,0.0003353233,0.2269866],"study_design_scores_gemma":[0.00024167376,0.000016215175,0.000017252218,0.00018509808,0.0000076286597,0.000017258704,0.0000049878445,0.52700245,0.46715274,0.0048100576,0.00042499148,0.00011966308],"about_ca_topic_score_codex":0.0002569396,"about_ca_topic_score_gemma":0.00007931234,"teacher_disagreement_score":0.5269067,"about_ca_system_score_codex":0.00010397121,"about_ca_system_score_gemma":0.00026848,"threshold_uncertainty_score":0.70681953},"labels":[],"label_agreement":null},{"id":"W3173881156","doi":"10.1109/cvprw53098.2021.00051","title":"Weighted Multi-Kernel Prediction Network for Burst Image Super-Resolution","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Samsung","keywords":"Artificial intelligence; Computer science; Kernel (algebra); Computer vision; Pixel; Optical flow; Pattern recognition (psychology); Discriminative model; Image resolution; Image processing; Image (mathematics); Mathematics","score_opus":0.020496141958271763,"score_gpt":0.2832949287998041,"score_spread":0.26279878684153235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173881156","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000114983486,0.0003640916,0.9958381,0.0009810126,0.0003259342,0.00022256373,0.000008961808,0.0015071065,0.0006372397],"genre_scores_gemma":[0.0055723223,0.000030601263,0.9921287,0.00034901782,0.00015160129,0.00009030528,0.000035582772,0.000016363241,0.0016255459],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876416,0.00003679547,0.00022049839,0.00047897673,0.00015857458,0.00034102018],"domain_scores_gemma":[0.9989312,0.00006655232,0.000061249746,0.00047364217,0.0004009872,0.00006635669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019555785,0.00013384531,0.00013212061,0.00004169969,0.00022985092,0.0001968357,0.00039546273,0.00007518265,0.000013631317],"category_scores_gemma":[0.0001047151,0.0001296973,0.000065965585,0.00041419282,0.000046298395,0.0011971885,0.00022145332,0.00010396083,0.000013012168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095903415,0.0012365561,0.002357168,0.0003542911,0.00013030444,0.000117972995,0.00091057905,0.0007794191,0.41346902,0.18324451,0.18080327,0.216501],"study_design_scores_gemma":[0.0003939941,0.00004773844,0.0006211495,0.000038498318,0.000007290456,0.000025149115,0.000010454189,0.9288977,0.033491734,0.024394216,0.011901692,0.00017038482],"about_ca_topic_score_codex":0.000008072008,"about_ca_topic_score_gemma":0.000011010024,"teacher_disagreement_score":0.9281183,"about_ca_system_score_codex":0.00007357025,"about_ca_system_score_gemma":0.00009043998,"threshold_uncertainty_score":0.52889025},"labels":[],"label_agreement":null},{"id":"W3174154150","doi":"10.1109/cvprw56347.2022.00059","title":"Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyramid Deformable Convolution and Deblurring-Reblurring Consistency","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Deblurring; Motion blur; Artificial intelligence; Kernel (algebra); Computer vision; Computer science; Image restoration; Pyramid (geometry); Convolution (computer science); Mathematics; Image (mathematics); Image processing; Artificial neural network; Geometry","score_opus":0.041808584474625446,"score_gpt":0.2843140973272218,"score_spread":0.24250551285259636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174154150","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18895647,0.00010882642,0.8085163,0.00023142758,0.0010543495,0.00051516376,0.000023777327,0.00041257543,0.0001811055],"genre_scores_gemma":[0.9011876,0.00015637859,0.097326234,0.00090771995,0.00018336659,0.00009135736,0.00006657503,0.000048053666,0.000032694865],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99637824,0.00025279183,0.00074484193,0.0012305651,0.0007063031,0.00068726006],"domain_scores_gemma":[0.9981838,0.00016106252,0.000501652,0.0005708938,0.00030716314,0.00027545728],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0007013435,0.00055135705,0.00051887735,0.00057181175,0.0015147139,0.00081973604,0.0006410962,0.0001663385,0.00010854634],"category_scores_gemma":[0.000025155192,0.0005846539,0.00011675897,0.00057801785,0.0001658761,0.0015137915,0.0011122399,0.00092901173,0.000018710856],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011079367,0.00018572812,0.00082684646,0.00012204972,0.000033349443,0.000063313535,0.00088145846,0.0016461145,0.0027020485,0.0001589107,0.00010135195,0.99316806],"study_design_scores_gemma":[0.001480318,0.0005884665,0.0006206525,0.0005575375,0.000033460194,0.00040271968,0.00015290387,0.98993295,0.00146191,0.0038867444,0.00014792845,0.0007343861],"about_ca_topic_score_codex":0.0001635687,"about_ca_topic_score_gemma":0.00004687617,"teacher_disagreement_score":0.99243367,"about_ca_system_score_codex":0.00026372436,"about_ca_system_score_gemma":0.00012001489,"threshold_uncertainty_score":0.9997852},"labels":[],"label_agreement":null},{"id":"W3177293728","doi":"10.1109/tip.2021.3092814","title":"Structure-Aware Motion Deblurring Using Multi-Adversarial Optimized CycleGAN","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Hong Kong Polytechnic University; Ministry of Science and Technology, Taiwan; National Natural Science Foundation of China","keywords":"Deblurring; Computer science; Artificial intelligence; Kernel (algebra); Image restoration; Pattern recognition (psychology); Image (mathematics); Adversarial system; Computer vision; Convolutional neural network; Image processing; Mathematics","score_opus":0.025025734974876014,"score_gpt":0.2987943317873849,"score_spread":0.27376859681250887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177293728","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016245798,0.00015483561,0.99590653,0.00016918618,0.00060854264,0.00019558988,0.000012702344,0.0012716239,0.00005639434],"genre_scores_gemma":[0.42011708,0.0000075041266,0.5796026,0.00013154968,0.000046639983,0.000011792585,0.0000020567902,0.000038148373,0.00004259986],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974864,0.00011940918,0.0004669482,0.00093700766,0.00044545968,0.0005447551],"domain_scores_gemma":[0.9983874,0.000056683748,0.0002294084,0.00063777046,0.00053606584,0.00015267578],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016875472,0.00038654357,0.00034751787,0.0002982276,0.00093559234,0.0009121969,0.0006472821,0.00016318038,0.000032459393],"category_scores_gemma":[0.0000392412,0.00042221445,0.0001477358,0.0010376651,0.00012657412,0.003863027,0.00001898502,0.00060634705,0.0000074825634],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046530615,0.0003042298,0.0000061355086,0.00020881167,0.0000370319,0.00014571386,0.00091803924,0.039689843,0.64451945,0.000023506766,0.000005884244,0.3140948],"study_design_scores_gemma":[0.0006570941,0.000013432755,0.0000046380405,0.00016236493,0.000027050726,0.00012714579,0.00005437288,0.55378,0.44435662,0.00051598507,0.000011175581,0.00029006996],"about_ca_topic_score_codex":0.000018235209,"about_ca_topic_score_gemma":0.000008826287,"teacher_disagreement_score":0.5140902,"about_ca_system_score_codex":0.0002763015,"about_ca_system_score_gemma":0.00038332815,"threshold_uncertainty_score":0.999823},"labels":[],"label_agreement":null},{"id":"W3180854701","doi":"10.3390/e23070881","title":"Compression Helps Deep Learning in Image Classification","year":2021,"lang":"en","type":"article","venue":"Entropy","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"JPEG; Artificial intelligence; Computer science; Image compression; Pattern recognition (psychology); Image (mathematics); Set (abstract data type); Compression ratio; Rank (graph theory); Artificial neural network; Compression (physics); JPEG 2000; Image processing; Mathematics","score_opus":0.014867459264055954,"score_gpt":0.2865191307496002,"score_spread":0.27165167148554425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180854701","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027004606,0.00041672323,0.9941124,0.00095160346,0.00007535269,0.000046929516,1.0585291e-7,0.0004324401,0.001264013],"genre_scores_gemma":[0.3484582,0.00004632712,0.6511547,0.00010512741,0.000022331638,0.000011262756,0.0000037229931,0.0000067160245,0.00019165143],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991124,0.000098651435,0.00015115723,0.00030353753,0.00015467044,0.00017953625],"domain_scores_gemma":[0.9994531,0.00004677312,0.00007398952,0.00030260172,0.00008759714,0.00003591228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000113346556,0.00007804939,0.00009473316,0.00006501944,0.00008037587,0.00013537312,0.00033389483,0.000035571033,0.000017828854],"category_scores_gemma":[0.00016945953,0.00007966969,0.000022748263,0.00034420993,0.000026471953,0.0006470352,0.00021131696,0.00021620675,0.000040577183],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000038068324,0.000072604,0.002050676,0.000016394757,0.0000016995881,0.00007583003,0.00040667065,0.000047116726,0.86583287,0.022869788,0.00022127853,0.108401276],"study_design_scores_gemma":[0.00037894616,0.000029710349,0.011350436,0.000105944324,0.0000019277868,0.00002713033,0.00009330473,0.6847175,0.2654391,0.029231226,0.008388504,0.00023629199],"about_ca_topic_score_codex":0.000002211216,"about_ca_topic_score_gemma":0.0000017286013,"teacher_disagreement_score":0.6846703,"about_ca_system_score_codex":0.00006374678,"about_ca_system_score_gemma":0.000037920363,"threshold_uncertainty_score":0.32488358},"labels":[],"label_agreement":null},{"id":"W3181468004","doi":"10.1109/tpami.2022.3157388","title":"Multi-Modality Deep Restoration of Extremely Compressed Face Videos","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Compression artifact; Computer vision; Convolutional neural network; Speech recognition; Image compression; Image processing","score_opus":0.03643387453943741,"score_gpt":0.3052555854592288,"score_spread":0.2688217109197914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181468004","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00069954706,0.00015354763,0.9983209,0.00039100673,0.000078335215,0.00014536003,0.00003956531,0.0001590183,0.000012751871],"genre_scores_gemma":[0.910807,0.00006397172,0.08883296,0.00016980835,0.0000033115557,0.00005487686,0.0000048117986,0.000008471594,0.000054801],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983984,0.0001764887,0.00041762416,0.00048729565,0.0003572657,0.00016291774],"domain_scores_gemma":[0.9989321,0.00010108884,0.00022458773,0.0005816056,0.000095255135,0.00006533777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030736395,0.00017035683,0.00028862432,0.0004404303,0.00033977465,0.00006481145,0.00064020546,0.000030170593,0.00007471501],"category_scores_gemma":[0.000007093904,0.00017086463,0.00015566281,0.0012035621,0.000077093144,0.00036408307,0.000022144757,0.00029423906,0.0000019862202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027835047,0.0005248338,0.00048749783,0.00003463285,0.00022437765,0.000007324298,0.0010655056,0.20965578,0.021640169,0.000074626376,0.0000052574437,0.76625216],"study_design_scores_gemma":[0.00006563106,0.000092997485,0.00041663487,0.0000066714797,0.00010239959,0.0000044026788,0.00004699914,0.81071687,0.18781313,0.00054911093,0.000032783133,0.00015237562],"about_ca_topic_score_codex":0.0006265838,"about_ca_topic_score_gemma":0.00043080686,"teacher_disagreement_score":0.91010743,"about_ca_system_score_codex":0.000056511784,"about_ca_system_score_gemma":0.000019914469,"threshold_uncertainty_score":0.6967657},"labels":[],"label_agreement":null},{"id":"W3186984374","doi":"10.1007/s11042-021-11121-6","title":"FSFN: feature separation and fusion network for single image super-resolution","year":2021,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Residual; Feature (linguistics); Artificial intelligence; Computation; Pattern recognition (psychology); Image (mathematics); Fusion; Separation (statistics); Layer (electronics); Image fusion; Resolution (logic); Feature detection (computer vision); Computer vision; Image processing; Algorithm; Machine learning","score_opus":0.022489522075781795,"score_gpt":0.30055255971773553,"score_spread":0.2780630376419537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186984374","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028739858,0.0013037148,0.99538815,0.0019171599,0.000033347937,0.0005344042,0.000024764313,0.00020890891,0.00030214075],"genre_scores_gemma":[0.009397914,0.0001545783,0.98891234,0.00020494667,0.00018247412,0.0008150067,0.00012441188,0.000009127781,0.00019917701],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927235,0.000016575073,0.000115091665,0.00035445267,0.00007707435,0.00016445787],"domain_scores_gemma":[0.9993222,0.00013598347,0.000054598015,0.00025738066,0.00016788807,0.0000619867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009164611,0.000096549804,0.000101105274,0.000021287982,0.00032608272,0.00027975158,0.00012473723,0.000067110355,0.0000016882854],"category_scores_gemma":[0.00005312454,0.00009616511,0.000021434857,0.00021433058,0.000058236285,0.00060241105,0.00013353961,0.00007553802,0.000002252234],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005157606,0.000082091115,0.0000832736,0.000046381374,0.000005327176,0.0000012710818,0.0001882829,0.000024018416,0.3676148,0.0093800835,0.0052264063,0.6173429],"study_design_scores_gemma":[0.0006872619,0.0000863406,0.001639373,0.00007212165,0.000028396056,0.00006542813,0.000048541842,0.5856127,0.06325825,0.040123545,0.30792338,0.00045464007],"about_ca_topic_score_codex":0.000001592442,"about_ca_topic_score_gemma":0.000005607658,"teacher_disagreement_score":0.6168883,"about_ca_system_score_codex":0.00001908396,"about_ca_system_score_gemma":0.000030271634,"threshold_uncertainty_score":0.39214996},"labels":[],"label_agreement":null},{"id":"W3190877284","doi":"10.5201/ipol.2021.358","title":"A Mathematical Analysis and Implementation of Residual Interpolation Demosaicking Algorithms","year":2021,"lang":"en","type":"article","venue":"Image Processing On Line","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; China Scholarship Council; McMaster University; École Normale Supérieure","keywords":"Demosaicing; Interpolation (computer graphics); Residual; Algorithm; Computer science; Artificial intelligence; Stairstep interpolation; Computer vision; Pattern recognition (psychology); Image processing; Linear interpolation; Image (mathematics); Color image","score_opus":0.02133015825774585,"score_gpt":0.36546182072092925,"score_spread":0.3441316624631834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190877284","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044482914,0.0002661764,0.9939708,0.00091591943,0.000014274738,0.0000667836,0.000003444249,0.00018397388,0.00013035492],"genre_scores_gemma":[0.30782893,0.000009547287,0.691995,0.000101795325,0.000022044673,0.000007487226,0.000009003912,0.000008004594,0.00001816617],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987316,0.00005044762,0.00038857496,0.00038603504,0.00027470826,0.00016860885],"domain_scores_gemma":[0.99897236,0.00008101923,0.000257087,0.00027271276,0.0003707019,0.00004611499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003129473,0.00012981273,0.00024321991,0.0002549607,0.00010050758,0.00024091685,0.00021697315,0.000040051287,0.000015422605],"category_scores_gemma":[0.00016879177,0.00012475191,0.00004447574,0.0010872498,0.0000716284,0.0009484656,0.0001921117,0.000118065975,0.0000014507849],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009382825,0.0001231041,0.0008264438,0.0002671006,0.000075860284,0.000026586744,0.001730735,0.000012796056,0.10067613,0.0024838087,0.000027989203,0.89374006],"study_design_scores_gemma":[0.00028894513,0.000090867674,0.000815658,0.00018125393,0.000111292,0.000028734392,0.00023954353,0.5822458,0.38379425,0.031995013,0.000018470599,0.00019014347],"about_ca_topic_score_codex":0.000004967617,"about_ca_topic_score_gemma":0.00000556506,"teacher_disagreement_score":0.8935499,"about_ca_system_score_codex":0.000025447798,"about_ca_system_score_gemma":0.000106892374,"threshold_uncertainty_score":0.5087235},"labels":[],"label_agreement":null},{"id":"W3194484130","doi":"10.1109/tmm.2021.3102401","title":"Learning-Based Quality Assessment for Image Super-Resolution","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Image quality; Feature extraction; Database; Data mining; Feature (linguistics); Pattern recognition (psychology); Artificial neural network; Quality (philosophy); Image (mathematics); Image resolution; Deep learning; Generalization","score_opus":0.03562366763511123,"score_gpt":0.3555343833712776,"score_spread":0.31991071573616636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194484130","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003245039,0.000025985251,0.99656874,0.0013263217,0.0004321971,0.00026474035,0.000022102617,0.00091423385,0.0001211892],"genre_scores_gemma":[0.21060371,0.000008242398,0.7885842,0.0002280436,0.000026910859,0.00020359093,0.00001105255,0.000018767005,0.00031552505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834985,0.00017263574,0.00029910548,0.0005447978,0.00031709994,0.000316506],"domain_scores_gemma":[0.9983804,0.00049879093,0.00009319509,0.0005230356,0.0004002457,0.00010431148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038543425,0.0001832319,0.00020215141,0.000118820026,0.00035417642,0.00015080275,0.00036191833,0.00009465655,0.000035524245],"category_scores_gemma":[0.00008352407,0.00019674798,0.00015789107,0.00035460322,0.00008336848,0.00061075785,0.000003646719,0.00037553706,0.000021665799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007104942,0.0014135777,0.000031578114,0.00018422393,0.000052878837,0.000035428344,0.0006267314,0.03067356,0.42173657,0.00030963615,0.0005506982,0.5443141],"study_design_scores_gemma":[0.00061160896,0.000120031276,0.00010603443,0.000028423636,0.000010742627,0.0000040489676,0.000023242508,0.6949009,0.30230036,0.0006705475,0.0010245377,0.00019958471],"about_ca_topic_score_codex":0.000014543669,"about_ca_topic_score_gemma":0.000019045987,"teacher_disagreement_score":0.6642273,"about_ca_system_score_codex":0.00017355321,"about_ca_system_score_gemma":0.00032003262,"threshold_uncertainty_score":0.80231494},"labels":[],"label_agreement":null},{"id":"W3195038451","doi":"10.1109/access.2021.3104526","title":"DRVI: Dual Refinement for Video Interpolation","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion interpolation; Computer science; Interpolation (computer graphics); Artificial intelligence; Computer vision; Frame (networking); Frame rate; Haar; Haar wavelet; Discrete wavelet transform; Image scaling; Process (computing); Algorithm; Wavelet; Wavelet transform; Motion (physics); Video processing; Image (mathematics); Video tracking; Image processing; Block-matching algorithm","score_opus":0.0466808881038223,"score_gpt":0.3688369905730178,"score_spread":0.32215610246919546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195038451","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011371213,0.0001243934,0.99568003,0.001224498,0.00045484488,0.00012198162,0.0000023076127,0.00036672637,0.0008880856],"genre_scores_gemma":[0.17930843,0.000007511459,0.81876916,0.0014293827,0.00011654911,0.00009830834,0.0000045534393,0.000011313967,0.00025479795],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99914193,0.000019406294,0.00017888757,0.00033964362,0.00014456191,0.0001755659],"domain_scores_gemma":[0.99913037,0.000068575064,0.00009415547,0.00042098464,0.00024954032,0.00003635236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013037714,0.00009007093,0.00010025753,0.000056741326,0.00009455119,0.00040771553,0.0006554713,0.000027603059,0.000011393883],"category_scores_gemma":[0.00010958083,0.000090329704,0.000038687187,0.00027362708,0.000018151362,0.001496282,0.00029211794,0.00006554569,0.000005959628],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046918554,0.0003428411,0.0010694291,0.00031657718,0.00005966092,0.00011581527,0.00093483186,0.00010620366,0.33080667,0.047315314,0.07210557,0.54678017],"study_design_scores_gemma":[0.00043766145,0.00007005955,0.0002865344,0.00010978846,0.000009473506,0.000033813158,0.000013037177,0.13614757,0.6879412,0.13645652,0.038158953,0.00033542424],"about_ca_topic_score_codex":0.000005524955,"about_ca_topic_score_gemma":0.000010611536,"teacher_disagreement_score":0.5464448,"about_ca_system_score_codex":0.00003979595,"about_ca_system_score_gemma":0.00008221107,"threshold_uncertainty_score":0.39316103},"labels":[],"label_agreement":null},{"id":"W3196659706","doi":"","title":"Ghost-DeblurGAN and Its Application to Fiducial Marker System","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Fiducial marker; Artificial intelligence; Deblurring; Computer vision; Computer science; Motion (physics); Scale (ratio); Image (mathematics); Image restoration; Image processing; Physics","score_opus":0.03416087082682235,"score_gpt":0.19644387845003558,"score_spread":0.16228300762321324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196659706","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04479259,0.00015434904,0.9525494,0.00015596008,0.0001932457,0.00044065673,0.0000054512857,0.0008047759,0.0009035606],"genre_scores_gemma":[0.93481314,0.000049909875,0.06453292,0.00012459455,0.00005873268,0.000006813119,0.000008200326,0.000020416977,0.0003852852],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980529,0.00008581904,0.00017283353,0.0013237646,0.00009260917,0.0002721079],"domain_scores_gemma":[0.99825555,0.0000447335,0.00018589012,0.0010245248,0.00030849237,0.00018081367],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019502996,0.00026791284,0.00028785897,0.00021317268,0.00016164019,0.00021920424,0.0013098053,0.00021351426,0.0000032595053],"category_scores_gemma":[0.000050555405,0.0003344167,0.00007066752,0.0006095359,0.000035488083,0.0005460143,0.0032573943,0.00034577725,0.000025509922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002322988,0.00052218087,0.0039694714,0.004786368,0.00040273025,0.0025697767,0.004313819,0.02991256,0.03150741,0.8433833,0.0015349275,0.07686516],"study_design_scores_gemma":[0.0002501187,0.000044478144,0.00093098625,0.00051202497,0.000056111207,0.000034960933,0.0001716338,0.9810038,0.005771117,0.009318218,0.0010482045,0.00085831777],"about_ca_topic_score_codex":0.00003450709,"about_ca_topic_score_gemma":0.000012271066,"teacher_disagreement_score":0.9510913,"about_ca_system_score_codex":0.0002687577,"about_ca_system_score_gemma":0.0001659436,"threshold_uncertainty_score":0.9999108},"labels":[],"label_agreement":null},{"id":"W3200637159","doi":"10.1109/bmsb53066.2021.9547068","title":"Multi-Kernel Deformable 3D Convolution for Video Super-Resolution","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Kernel (algebra); Artificial intelligence; Computer science; Convolution (computer science); Computer vision; Fuse (electrical); Image resolution; Motion estimation; Pattern recognition (psychology); Artificial neural network; Mathematics; Engineering","score_opus":0.02877638300091884,"score_gpt":0.29608315454486517,"score_spread":0.26730677154394633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200637159","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001890478,0.00039316676,0.99687535,0.0006023271,0.0001673968,0.00017768262,0.000002648561,0.0008914027,0.0007009666],"genre_scores_gemma":[0.022780593,0.000014268091,0.9743511,0.00072999106,0.000031334643,0.00007624721,0.000009357945,0.000010774041,0.0019963132],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989443,0.00002355767,0.00019769152,0.00038297116,0.00014100554,0.0003104935],"domain_scores_gemma":[0.99902236,0.000050804083,0.000059110847,0.00042654303,0.00038348822,0.00005767966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001991624,0.00011471525,0.00012159428,0.000053538282,0.00019406807,0.0001471511,0.00037994687,0.000052182204,0.00001700282],"category_scores_gemma":[0.00019630279,0.00010985936,0.000055695367,0.00025731075,0.000039289553,0.0015731149,0.00024048361,0.000078661695,0.0000218189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050890747,0.00072650705,0.00084199844,0.00036804643,0.000055118107,0.000047673362,0.00086752075,0.0010169158,0.6315843,0.16103387,0.02148459,0.1819226],"study_design_scores_gemma":[0.00036434035,0.000031890977,0.000111255635,0.000020968886,0.0000037564025,0.000034365596,0.000015606533,0.8766884,0.094289884,0.009651879,0.018630102,0.00015750229],"about_ca_topic_score_codex":0.000024196406,"about_ca_topic_score_gemma":0.000028782515,"teacher_disagreement_score":0.8756715,"about_ca_system_score_codex":0.00010842623,"about_ca_system_score_gemma":0.00014186362,"threshold_uncertainty_score":0.44799346},"labels":[],"label_agreement":null},{"id":"W3205467587","doi":"10.1109/tip.2021.3120678","title":"High Frequency Detail Accentuation in CNN Image Restoration","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Image restoration; Computer science; Image (mathematics); Feature (linguistics); Ground truth; Pattern recognition (psychology); Computer vision; Outlier; Feature extraction; Image processing","score_opus":0.017753508057438718,"score_gpt":0.28498068067196414,"score_spread":0.26722717261452544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205467587","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027611325,0.0002653408,0.99384916,0.0012731821,0.0002966059,0.00018987054,0.0000038894736,0.00079688005,0.00056392205],"genre_scores_gemma":[0.4328918,0.00004342953,0.56664306,0.00019299642,0.000025329715,0.000068814414,0.0000038062399,0.000023238059,0.00010754678],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997742,0.00013031038,0.00051909196,0.0007614245,0.00043722024,0.0004099545],"domain_scores_gemma":[0.9984862,0.000068545814,0.00020653565,0.00059120666,0.000559469,0.00008802198],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030872147,0.00027218295,0.0002475191,0.00038822144,0.00039467035,0.0008049029,0.00054878445,0.000120190896,0.000038721293],"category_scores_gemma":[0.00007111833,0.00030676482,0.00007248967,0.0016247245,0.00010284889,0.006228276,0.000008991035,0.00051466294,0.00005233561],"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":[0.000015828618,0.0003550557,0.00001876767,0.00013495232,0.000006858907,0.00013298827,0.0007353714,0.00044462466,0.6079556,0.00031234205,0.00004392588,0.38984373],"study_design_scores_gemma":[0.00064180535,0.00005535632,0.00023207445,0.00035461446,0.000017944616,0.00006228197,0.00009429763,0.08577027,0.88882154,0.023395594,0.000048311613,0.0005058816],"about_ca_topic_score_codex":0.000031769243,"about_ca_topic_score_gemma":0.000065136155,"teacher_disagreement_score":0.43013066,"about_ca_system_score_codex":0.0003205496,"about_ca_system_score_gemma":0.0004653911,"threshold_uncertainty_score":0.9999384},"labels":[],"label_agreement":null},{"id":"W3207388182","doi":"10.3390/rs13204044","title":"Model Specialization for the Use of ESRGAN on Satellite and Airborne Imagery","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; University of Arizona","keywords":"Hallucinating; Computer science; Generalization; Artificial intelligence; Generative grammar; Satellite imagery; Remote sensing; Mathematics; Geography","score_opus":0.08002190815484558,"score_gpt":0.2966182921911709,"score_spread":0.21659638403632533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207388182","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012170282,0.00026926413,0.99728733,0.0008818951,0.000053094536,0.000095797244,8.6782273e-7,0.00010143485,0.00009325759],"genre_scores_gemma":[0.043708973,0.00021819578,0.9554209,0.000518471,0.00003343495,1.4254196e-8,0.0000013367251,0.000011372431,0.00008731986],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938816,0.000025804182,0.0001385205,0.00022221486,0.000109884546,0.00011542529],"domain_scores_gemma":[0.99908674,0.00024107698,0.00008708209,0.00035360016,0.0002113912,0.000020123754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011519835,0.00007613754,0.00010112668,0.00003936976,0.00010389915,0.00013735647,0.00009150932,0.00002841277,1.0643612e-7],"category_scores_gemma":[0.0002922659,0.000062657346,0.000028293958,0.00019383272,0.000050181756,0.00033385758,0.000097035954,0.000056260393,2.6852794e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006710054,0.0000046032455,6.516681e-7,0.00001975704,0.000003852924,0.000004930627,0.00024161479,0.002225906,0.06994534,0.0014863414,0.00007430197,0.925986],"study_design_scores_gemma":[0.00005391315,0.000010541725,0.0000151236445,0.00007990263,0.0000048905654,0.00001622796,0.000005691643,0.879179,0.100521795,0.019189024,0.00086200034,0.00006186476],"about_ca_topic_score_codex":0.0000033716065,"about_ca_topic_score_gemma":0.0000023812268,"teacher_disagreement_score":0.9259241,"about_ca_system_score_codex":0.000020184281,"about_ca_system_score_gemma":0.00004792494,"threshold_uncertainty_score":0.25550923},"labels":[],"label_agreement":null},{"id":"W3208275223","doi":"10.48550/arxiv.2111.00361","title":"Functional Neural Networks for Parametric Image Restoration Problems","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image restoration; Computer science; Parametric statistics; Artificial intelligence; Artificial neural network; Image (mathematics); JPEG; Image quality; Noise reduction; Machine learning; Algorithm; Mathematics; Image processing; Statistics","score_opus":0.08400814661701858,"score_gpt":0.21278713153972126,"score_spread":0.12877898492270268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208275223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043186354,0.00024441918,0.9932353,0.00014078579,0.0006615698,0.0005089858,0.000005191266,0.0007349331,0.00015017149],"genre_scores_gemma":[0.73716384,0.00007156717,0.2620121,0.00009634111,0.000114866205,0.000011922563,0.00008191204,0.000022805145,0.0004246434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979964,0.00009747757,0.00022759347,0.0012214812,0.00010516437,0.00035189258],"domain_scores_gemma":[0.99779004,0.00016628213,0.0003562512,0.00097581063,0.00060643384,0.00010520048],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002544412,0.00029667732,0.0002838095,0.0003313229,0.00023595964,0.00044870755,0.0011496951,0.0002735242,0.000007553957],"category_scores_gemma":[0.00015029756,0.0003637062,0.00019966309,0.0011606879,0.00010187205,0.0013803052,0.0013769743,0.0005844048,0.000003677377],"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":[0.000033282417,0.000116797855,0.00033791145,0.00017986014,0.000046227942,0.00009266009,0.000059601934,0.97344375,0.00022940707,0.020468146,0.0014611636,0.0035311717],"study_design_scores_gemma":[0.00023789547,0.00005029529,0.00024238174,0.00006350591,0.000031539057,0.0000068205863,0.000011878262,0.9523944,0.00015488503,0.04629894,0.00015128488,0.0003561284],"about_ca_topic_score_codex":0.000019260846,"about_ca_topic_score_gemma":0.000010365028,"teacher_disagreement_score":0.7328452,"about_ca_system_score_codex":0.000266998,"about_ca_system_score_gemma":0.00020697594,"threshold_uncertainty_score":0.9998815},"labels":[],"label_agreement":null},{"id":"W3211697047","doi":"10.1109/tbc.2021.3126275","title":"SRNMSM: A Deep Light-Weight Image Super Resolution Network Using Multi-Scale Spatial and Morphological Feature Generating Residual Blocks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Broadcasting","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Residual; Artificial intelligence; Computer science; Block (permutation group theory); Convolutional neural network; Pattern recognition (psychology); Feature (linguistics); Fuse (electrical); Benchmark (surveying); Image resolution; Computer vision; Algorithm; Mathematics; Engineering","score_opus":0.02469682393135027,"score_gpt":0.2713106760829818,"score_spread":0.24661385215163154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211697047","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020966394,0.0006332349,0.9766873,0.00046878797,0.00039756251,0.00016201114,0.000005476301,0.00060975034,0.000069490125],"genre_scores_gemma":[0.19172642,0.000030475974,0.8075882,0.00021295306,0.0002740823,0.000020754369,0.0000022431689,0.000029598206,0.00011525097],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976066,0.0002041384,0.00036789334,0.00089492905,0.00032691608,0.0005995673],"domain_scores_gemma":[0.99884844,0.00015132017,0.00013857533,0.0004692403,0.0002495741,0.00014284026],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030820884,0.00031801133,0.00029817838,0.000117761505,0.0011553017,0.0003999459,0.00032005226,0.00022742595,0.000017983059],"category_scores_gemma":[0.000057384605,0.00031818816,0.00008971779,0.00063143234,0.00010943419,0.00083800027,0.000034690398,0.00076309935,0.00000326404],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003066679,0.00018166691,0.00010551145,0.000034884462,0.00002634629,0.00038861381,0.00071985234,0.026088616,0.8947738,0.000012925254,0.00012797034,0.07750918],"study_design_scores_gemma":[0.0003894543,0.00006030121,0.000064507316,0.00016324065,0.000029418043,0.00087685214,0.000043848777,0.8264147,0.17135836,0.0001592726,0.0001016886,0.0003383655],"about_ca_topic_score_codex":0.00003706374,"about_ca_topic_score_gemma":0.00008954167,"teacher_disagreement_score":0.80032605,"about_ca_system_score_codex":0.000120623,"about_ca_system_score_gemma":0.00009984359,"threshold_uncertainty_score":0.99992704},"labels":[],"label_agreement":null},{"id":"W3212325172","doi":"10.1109/tcsii.2021.3121667","title":"FPNet: A Deep Light-Weight Interpretable Neural Network Using Forward Prediction Filtering for Efficient Single Image Super Resolution","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Block (permutation group theory); Artificial neural network; Computer science; Residual; Artificial intelligence; Image (mathematics); Task (project management); Algorithm; Superresolution; Resolution (logic); Deep learning; Pattern recognition (psychology); Mathematics; Engineering","score_opus":0.021006194376466027,"score_gpt":0.24804720024319946,"score_spread":0.22704100586673343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212325172","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034768872,0.0009684342,0.9896848,0.00011115552,0.0033300489,0.00091124786,0.000066888824,0.0011970819,0.0002534479],"genre_scores_gemma":[0.8510083,0.000012182073,0.14768468,0.000106822794,0.00035687062,0.00044018807,0.000009713941,0.00008652233,0.00029473437],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964392,0.000219869,0.0008046027,0.0011316689,0.0005328996,0.00087176537],"domain_scores_gemma":[0.9977599,0.000171338,0.00027032223,0.0010701374,0.00053875137,0.00018958641],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040433838,0.00043898268,0.00051133655,0.00023354456,0.0011958601,0.00065600564,0.0007008229,0.00021277744,0.0000093199205],"category_scores_gemma":[0.000033079577,0.0004825838,0.00026499727,0.0007680325,0.00007666037,0.0016543001,0.000034603272,0.00037615892,0.0000047282356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003146642,0.0003129519,0.000002516916,0.00025759274,0.00006410334,0.000024920935,0.0015520212,0.39695472,0.59239036,0.00017090362,0.00037456286,0.007863876],"study_design_scores_gemma":[0.0004889488,0.00019498976,0.0000029521427,0.0007687159,0.000055919343,0.00026292435,0.00007058975,0.8211439,0.17349698,0.00013108272,0.0029755235,0.00040746355],"about_ca_topic_score_codex":0.000041521533,"about_ca_topic_score_gemma":0.000010016326,"teacher_disagreement_score":0.8475314,"about_ca_system_score_codex":0.0004889051,"about_ca_system_score_gemma":0.00014559743,"threshold_uncertainty_score":0.9997626},"labels":[],"label_agreement":null},{"id":"W3212563905","doi":"10.1051/0004-6361/202141166","title":"Deep transfer learning for blended source identification in galaxy survey data","year":2021,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Canadian Space Agency; Universidade de São Paulo; Centre National de la Recherche Scientifique; Commissariat à l'Énergie Atomique et aux Énergies Alternatives; Agence Nationale de la Recherche; European Space Agency","keywords":"Python (programming language); Transfer of learning; Computer science; Parametric statistics; Artificial intelligence; Deep learning; Identification (biology); Galaxy; Transfer function; Noise (video); Pattern recognition (psychology); Pixel; Data mining; Image (mathematics); Mathematics; Astrophysics; Physics; Statistics","score_opus":0.025597781724793343,"score_gpt":0.2708138972325072,"score_spread":0.24521611550771386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212563905","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007542705,0.0000848475,0.99206614,0.00008030345,0.000031722782,0.000095736905,0.0000061511255,0.00008193303,0.000010480961],"genre_scores_gemma":[0.39751095,0.0000053819867,0.6022581,0.000009268269,0.000022303859,0.0000133773765,0.0001339326,0.000007712415,0.000039019404],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990093,0.00007512619,0.0001754991,0.00046186443,0.00007818995,0.00020002104],"domain_scores_gemma":[0.999275,0.000089775436,0.000044818327,0.00046816416,0.00008657051,0.00003565642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002320113,0.00010248022,0.00012913358,0.00003274116,0.000109160064,0.0001849865,0.00047342718,0.000024943958,0.000001084298],"category_scores_gemma":[0.000048921585,0.00011836534,0.000019068177,0.00020748991,0.000033015254,0.001025794,0.00023434171,0.00014583851,0.0000015681306],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007539786,0.00004178598,0.0023064548,0.000015461823,0.000007518895,9.754499e-7,0.00016793037,0.001038781,0.0072555826,0.00073379977,0.000010466363,0.9884137],"study_design_scores_gemma":[0.0016163413,0.00018130384,0.055636015,0.0001152704,0.000029594841,0.000011382136,0.0003826361,0.8371585,0.07677819,0.009490256,0.017799228,0.00080130383],"about_ca_topic_score_codex":0.00000847899,"about_ca_topic_score_gemma":0.000014183086,"teacher_disagreement_score":0.9876124,"about_ca_system_score_codex":0.0000151550275,"about_ca_system_score_gemma":0.00007587303,"threshold_uncertainty_score":0.48267984},"labels":[],"label_agreement":null},{"id":"W3212933866","doi":"10.1093/mnras/stab3243","title":"Uncertainty-Aware Learning for Improvements in Image Quality of the Canada-France-Hawaii Telescope","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Herzberg Institute of Astrophysics; University of Toronto","funders":"Space Telescope Science Institute; University of Hawai'i; University of Southern California; National Aeronautics and Space Administration","keywords":"Observatory; Leverage (statistics); Scheduling (production processes); Machine learning; Physics; Computer science; Artificial intelligence","score_opus":0.037274793375522676,"score_gpt":0.21978430478786484,"score_spread":0.18250951141234217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212933866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.113498375,0.000033449527,0.8855006,0.00018299138,0.000091986156,0.00017867319,0.000006437661,0.000067174704,0.00044031077],"genre_scores_gemma":[0.97910905,0.0000117070595,0.01959355,0.00014880924,0.000007832389,0.0000015877085,0.0000022640681,0.000008012345,0.001117176],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888873,0.00011735581,0.0001957173,0.00044822943,0.00009484104,0.00025510608],"domain_scores_gemma":[0.9987925,0.00013134349,0.00020367104,0.00053354446,0.00029495815,0.00004393054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022898501,0.000118172036,0.0001866668,0.000046280496,0.00014470976,0.000028223416,0.00091048545,0.00004060512,0.000004501421],"category_scores_gemma":[0.00018009153,0.000115087365,0.00006773607,0.00080883113,0.000078029836,0.00043722312,0.00045496528,0.00018151832,4.7021595e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027608237,0.0009453737,0.26455677,0.0018074735,0.0002113671,0.0006124116,0.0016619675,0.15278174,0.22457327,0.28670937,0.0019221477,0.063942015],"study_design_scores_gemma":[0.003906936,0.00016082545,0.034304775,0.0004701274,0.00003517394,0.0000067440287,0.0016278658,0.7290184,0.15111114,0.07171125,0.006460354,0.0011864468],"about_ca_topic_score_codex":0.021838387,"about_ca_topic_score_gemma":0.049426466,"teacher_disagreement_score":0.8659071,"about_ca_system_score_codex":0.00022917536,"about_ca_system_score_gemma":0.00070195936,"threshold_uncertainty_score":0.9846753},"labels":[],"label_agreement":null},{"id":"W3216378395","doi":"10.18280/ts.380511","title":"Deep Learning Based Super Resolution and Classification Applications for Neonatal Thermal Images","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Konya Teknik Üniversitesi","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Deep learning; Generative adversarial network; Ground truth; Pattern recognition (psychology); Classifier (UML); Similarity (geometry); Peak signal-to-noise ratio; Superresolution; Image resolution; Artificial neural network; Computer vision; Data mining; Image (mathematics)","score_opus":0.019356626789173093,"score_gpt":0.2685702776911887,"score_spread":0.24921365090201558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216378395","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00066679873,0.00039947292,0.9971514,0.0010609949,0.000013986248,0.0002850779,0.0000030933124,0.00028226146,0.00013696584],"genre_scores_gemma":[0.40770385,0.000008476104,0.59169185,0.00015029962,0.000043729753,0.00031072035,0.00003492071,0.0000084939375,0.000047665875],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991242,0.000048989787,0.00016322504,0.00034509096,0.00014794694,0.00017053756],"domain_scores_gemma":[0.9994301,0.00010283307,0.000068830646,0.00017356216,0.00018051646,0.000044141074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002050936,0.0000965542,0.00008201346,0.000052797244,0.00025858672,0.00015209058,0.00020214476,0.000035411096,0.000016676158],"category_scores_gemma":[0.00002939836,0.00010038105,0.000032922853,0.00016216333,0.000054934146,0.00051109964,0.00006365263,0.00009077,0.0000028763523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021041571,0.00010307434,0.0004649928,0.0000775823,0.000012145053,0.0000034867073,0.00032432078,0.0011077081,0.4141223,0.013791757,0.0001452181,0.56982636],"study_design_scores_gemma":[0.00043444696,0.0000662375,0.003278232,0.000017970959,0.0000110545325,0.00000770646,0.000054099313,0.93342674,0.04866659,0.002981392,0.010887082,0.00016845492],"about_ca_topic_score_codex":8.1687716e-7,"about_ca_topic_score_gemma":0.0000010830879,"teacher_disagreement_score":0.93231905,"about_ca_system_score_codex":0.000037776597,"about_ca_system_score_gemma":0.00005424216,"threshold_uncertainty_score":0.40934202},"labels":[],"label_agreement":null},{"id":"W3217311030","doi":"10.1007/s11042-021-11697-z","title":"Capsule GAN for prostate MRI super-resolution","year":2021,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Artificial intelligence; Classifier (UML); Metric (unit); Margin (machine learning); Similarity (geometry); Prostate cancer; Prostate; Pattern recognition (psychology); Machine learning; Cancer; Medicine; Image (mathematics)","score_opus":0.02197185378253986,"score_gpt":0.2897010581938506,"score_spread":0.26772920441131076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217311030","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013266936,0.0004782648,0.99605155,0.0021535705,0.00002535147,0.00051901134,0.000046903195,0.00028297462,0.00030968475],"genre_scores_gemma":[0.015712038,0.00012275921,0.9818026,0.00021617567,0.00007107146,0.0015881228,0.0000728921,0.000009811005,0.00040455064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992618,0.000010373552,0.00013560358,0.00034122166,0.00007695161,0.00017405776],"domain_scores_gemma":[0.9992938,0.0000724662,0.000043869157,0.00034174926,0.00018129483,0.00006683426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008007528,0.00008370567,0.00008987143,0.000026255859,0.00021911456,0.0001816601,0.00020933368,0.000036864363,0.000002737092],"category_scores_gemma":[0.00006016363,0.00008378688,0.000025683707,0.0002028396,0.0000539714,0.0004297183,0.00009177337,0.000060930193,0.000008228098],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020926639,0.00012076674,0.000076941964,0.000059232683,0.000007632862,0.0000017453193,0.00045241186,0.000025456746,0.1433217,0.021549385,0.0026358417,0.8317468],"study_design_scores_gemma":[0.0005641992,0.000041278003,0.00086493004,0.00003078487,0.000013629851,0.000024922892,0.000059078466,0.47524276,0.15862201,0.05191463,0.312266,0.00035576473],"about_ca_topic_score_codex":0.0000035917858,"about_ca_topic_score_gemma":0.0000037153168,"teacher_disagreement_score":0.83139104,"about_ca_system_score_codex":0.000018750377,"about_ca_system_score_gemma":0.00006409352,"threshold_uncertainty_score":0.341673},"labels":[],"label_agreement":null},{"id":"W4206753990","doi":"10.23919/eusipco54536.2021.9616283","title":"End-to-End Generative Adversarial Face Hallucination Through Residual In Internal Dense Network","year":2021,"lang":"en","type":"article","venue":"2021 29th European Signal Processing Conference (EUSIPCO)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; Confederation College; Lakehead University","funders":"University of British Columbia","keywords":"Hallucinating; Discriminator; Artificial intelligence; Computer science; Face (sociological concept); Face hallucination; Residual; Pixel; Computer vision; Generator (circuit theory); Inpainting; Exploit; Pattern recognition (psychology); Image (mathematics); Facial recognition system; Face detection; Algorithm","score_opus":0.034582219154815845,"score_gpt":0.291294270939618,"score_spread":0.25671205178480216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206753990","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023954734,0.0007740332,0.9763094,0.0014937049,0.00030939883,0.00026517705,0.000007333103,0.00037147623,0.01807399],"genre_scores_gemma":[0.5370399,0.00003602474,0.46034443,0.0009784086,0.0004892056,0.000018271943,0.000022025424,0.000048271435,0.0010234889],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951501,0.00090554304,0.0007969574,0.001503341,0.00079240586,0.0008516924],"domain_scores_gemma":[0.9976024,0.00015642753,0.00041184115,0.0006563063,0.00097199937,0.00020100652],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0010267781,0.00051193376,0.00049426634,0.00019195469,0.0004207698,0.0013674153,0.0016050254,0.00011157385,0.00026624845],"category_scores_gemma":[0.00039019063,0.0005415618,0.00008138503,0.0014638104,0.00019015244,0.00253011,0.0014234707,0.00078864914,0.00013619768],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017376446,0.0003332699,0.00042536276,0.00014925093,0.00005974433,0.0027722318,0.015678035,0.0069427723,0.06972872,0.010689194,0.002603311,0.89044434],"study_design_scores_gemma":[0.005388177,0.0012142971,0.005571492,0.00671213,0.00015022406,0.0010082809,0.0020083145,0.70772904,0.16564298,0.0624077,0.036574606,0.005592764],"about_ca_topic_score_codex":0.000035892568,"about_ca_topic_score_gemma":0.00008796564,"teacher_disagreement_score":0.8848516,"about_ca_system_score_codex":0.00022607665,"about_ca_system_score_gemma":0.0012273542,"threshold_uncertainty_score":0.9997036},"labels":[],"label_agreement":null},{"id":"W4207085673","doi":"10.1101/2022.01.24.22269144","title":"Super-Resolution of Magnetic Resonance Images Acquired Under Clinical Protocols using Deep Attention-based Method","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Medical Research Council; UK Dementia Research Institute; China Scholarship Council; Fondation Leducq; University of Edinburgh; Stroke Association; Alzheimer's Society; Wellcome Trust; Mrs Gladys Row Fogo Charitable Trust; Weston Brain Institute; UK Research and Innovation; Edinburgh and Lothians Health Foundation","keywords":"Interpretability; Fluid-attenuated inversion recovery; Artificial intelligence; Magnetic resonance imaging; Neuroimaging; Computer science; Feature (linguistics); Image quality; Pattern recognition (psychology); Ground truth; Nuclear medicine; Computer vision; Medicine; Radiology; Image (mathematics)","score_opus":0.0898844489885891,"score_gpt":0.4264848541626274,"score_spread":0.3366004051740383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207085673","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032487395,0.0014251415,0.9872689,0.0004548614,0.0003566002,0.0065137744,0.000019961006,0.000629131,0.00008288899],"genre_scores_gemma":[0.012838423,0.000022291422,0.9808687,0.0002181503,0.00008553069,0.005788636,0.000014183938,0.00005944216,0.000104654355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99455124,0.0013390969,0.0013117883,0.0014571338,0.00085656345,0.0004841613],"domain_scores_gemma":[0.995855,0.00051764795,0.0008823484,0.0021729579,0.00045749656,0.00011458396],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0029687025,0.00043502072,0.0007739309,0.0003220562,0.00023305617,0.00017807195,0.0028534424,0.00029789656,0.00007903971],"category_scores_gemma":[0.0007062212,0.00045926915,0.00037068158,0.00066241354,0.00033228862,0.0004009837,0.0029758536,0.00076738326,0.0000034054765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006250381,0.0039073634,0.18418732,0.005462543,0.00018168431,0.00025562604,0.000780701,0.052922063,0.21313162,0.0082667265,0.001082351,0.529197],"study_design_scores_gemma":[0.0006855196,0.00030514167,0.025652647,0.00076434657,0.000051717114,0.000012885258,0.000016916889,0.9303358,0.009569072,0.030155314,0.0017350533,0.0007155956],"about_ca_topic_score_codex":0.000039347415,"about_ca_topic_score_gemma":0.000003267861,"teacher_disagreement_score":0.87741375,"about_ca_system_score_codex":0.00023136497,"about_ca_system_score_gemma":0.000678647,"threshold_uncertainty_score":0.9997859},"labels":[],"label_agreement":null},{"id":"W4213125316","doi":"10.1093/pasj/psab111","title":"Galaxy morphologies revealed with Subaru HSC and super-resolution techniques. I. Major merger fractions of<i>L</i>UV ∼ 3–15 L*UV dropout galaxies at<i>z</i>∼ 4–7","year":2021,"lang":"en","type":"article","venue":"Publications of the Astronomical Society of Japan","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Planetary Science Division; Japan Society for the Promotion of Science; Science Mission Directorate; Horizon 2020 Framework Programme; H2020 European Research Council; Smithsonian Astrophysical Observatory; National Central University; Ministry of Education, Culture, Sports, Science and Technology; Queen's University; Cabinet Office, Government of Japan; National Astronomical Observatory of Japan; National Aeronautics and Space Administration; Danmarks Grundforskningsfond; Toray Science Foundation; University of Tokyo; High Energy Accelerator Research Organization; National Research Foundation; Space Telescope Science Institute; University of Hawai'i; Los Alamos National Laboratory; Johns Hopkins University; Princeton University; Max-Planck-Institut für Astronomie; University of Edinburgh; Eötvös Loránd Tudományegyetem; Academia Sinica; European Commission; Queen's University Belfast; Durham University; Japan Science and Technology Agency; Smithsonian Institution; University of Maryland; National Science Foundation","keywords":"Physics; Astrophysics; Galaxy; Hubble space telescope; Luminosity; Subaru Telescope; Astronomy; Spectrograph; Spectral line","score_opus":0.011441866640656204,"score_gpt":0.24125998308242588,"score_spread":0.2298181164417697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213125316","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57939196,0.00044540328,0.402435,0.016844034,0.00003951512,0.00034797756,0.000034027315,0.00030086352,0.0001612304],"genre_scores_gemma":[0.5176063,0.000029835688,0.48205212,0.000019365369,0.000011149039,0.000047591926,0.000008910527,0.000009987094,0.0002147118],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99854225,0.000077076955,0.00047071138,0.00041569036,0.0002427969,0.00025145942],"domain_scores_gemma":[0.99766237,0.00014731086,0.0004860865,0.0011093683,0.0005270279,0.00006785344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029336516,0.00017333876,0.00032755133,0.00006075187,0.00026884512,0.000057448757,0.0009879769,0.000104738494,0.000022593316],"category_scores_gemma":[0.0001910998,0.00013994375,0.00020756215,0.00057333946,0.00092698564,0.0007621541,0.00093710766,0.00022628266,0.0000011866479],"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":[0.000025093621,0.00043886673,0.699653,0.00014333914,0.00019438952,1.9809562e-8,0.0007588802,0.00007297907,0.27177954,0.0064706164,0.016674818,0.0037884337],"study_design_scores_gemma":[0.00034110053,0.000098923,0.6534068,0.00010216145,0.00007233686,0.000021481384,0.0002950859,0.005792609,0.33480522,0.0029389495,0.0018412987,0.0002840087],"about_ca_topic_score_codex":0.000058210622,"about_ca_topic_score_gemma":0.000010784741,"teacher_disagreement_score":0.07961713,"about_ca_system_score_codex":0.00012595645,"about_ca_system_score_gemma":0.00019805714,"threshold_uncertainty_score":0.570674},"labels":[],"label_agreement":null},{"id":"W4220798134","doi":"10.1007/s11042-022-12584-x","title":"Toward image super-resolution based on local regression and nonlocal means","year":2022,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec","funders":"","keywords":"Computer science; Artificial intelligence; Superresolution; Image (mathematics); Resolution (logic); Image resolution; Computer vision; Pattern recognition (psychology); Regression; Image quality; Data mining; Statistics; Mathematics","score_opus":0.02466935594799918,"score_gpt":0.2791279799237686,"score_spread":0.2544586239757694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220798134","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013383552,0.00015205798,0.9960361,0.0025523293,0.000025036306,0.00036505866,0.000039700215,0.00030467514,0.00039120586],"genre_scores_gemma":[0.21483412,0.000025364923,0.78349805,0.00052922673,0.00003971412,0.0009770468,0.000041021114,0.000012364556,0.000043089483],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896705,0.00004571386,0.00014744503,0.00043193004,0.00022782476,0.00018005526],"domain_scores_gemma":[0.9992938,0.00014566064,0.000057042464,0.00035953955,0.000047871836,0.00009610125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016437864,0.00012470555,0.00011286161,0.00007938013,0.0005027002,0.00012030958,0.00032898496,0.000034186494,0.000012772935],"category_scores_gemma":[0.000029832303,0.00011434597,0.00002320721,0.0002500643,0.00016481034,0.0003350368,0.00032893618,0.00021027717,0.0000051925813],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001411292,0.00015374906,0.000057845326,0.00002234883,0.0000022803426,0.0000047182484,0.00023782052,0.00027623642,0.008255835,0.0038808957,0.00092039636,0.98617375],"study_design_scores_gemma":[0.00027932524,0.00007618141,0.00023250128,0.000013170811,0.0000041398885,0.000011197673,0.000055994467,0.97312945,0.0024417376,0.0021287333,0.021481063,0.00014651626],"about_ca_topic_score_codex":0.000010575413,"about_ca_topic_score_gemma":0.0000010566622,"teacher_disagreement_score":0.98602724,"about_ca_system_score_codex":0.00006468677,"about_ca_system_score_gemma":0.000044584398,"threshold_uncertainty_score":0.46628934},"labels":[],"label_agreement":null},{"id":"W4224219854","doi":"10.1049/ipr2.12497","title":"Multi‐scale GAN with residual image learning for removing heterogeneous blur","year":2022,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Residual; Computer science; Artificial intelligence; Scale (ratio); Image (mathematics); Computer vision; Pattern recognition (psychology); Algorithm; Physics","score_opus":0.013893813859736438,"score_gpt":0.27996854389353276,"score_spread":0.26607473003379634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224219854","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00474092,0.0007035981,0.9911714,0.00057988305,0.000079053294,0.00046996956,0.0000065637087,0.0019128943,0.0003356993],"genre_scores_gemma":[0.20095919,0.0000048908364,0.79777837,0.00031352154,0.00008131804,0.00030248257,0.00001168587,0.00009454753,0.00045397284],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968909,0.0001443813,0.00043378206,0.0010937687,0.00060879684,0.00082834356],"domain_scores_gemma":[0.99830294,0.00011621489,0.00044774573,0.00058930303,0.00040881283,0.0001350085],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00072537217,0.00038725493,0.00035806972,0.00024841417,0.002053494,0.001006617,0.0014979885,0.000055934554,0.000012180339],"category_scores_gemma":[0.00020641359,0.00038921606,0.00008248213,0.00075703097,0.00019470802,0.0024324446,0.0008096298,0.00068585173,0.0000049343394],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002032279,0.00033564775,0.00054828293,0.0006473214,0.000031786738,0.00035794397,0.0053907936,0.0035771884,0.7732032,0.000026166084,0.0005354563,0.215143],"study_design_scores_gemma":[0.0014011421,0.000618618,0.00008202401,0.00022022247,0.000038213315,0.0009753797,0.00070910197,0.8186303,0.17083386,0.0018962178,0.0034812684,0.0011136768],"about_ca_topic_score_codex":0.000009941037,"about_ca_topic_score_gemma":0.0000053526237,"teacher_disagreement_score":0.8150531,"about_ca_system_score_codex":0.00020543652,"about_ca_system_score_gemma":0.00031798525,"threshold_uncertainty_score":0.999856},"labels":[],"label_agreement":null},{"id":"W4226263340","doi":"10.1109/access.2022.3140466","title":"Learning Super-Resolution of Environment Matting of Transparent Objects From a Single Image","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Computer science; Computer vision; Image resolution; Resolution (logic); Artificial intelligence","score_opus":0.037206747491948716,"score_gpt":0.2863705385783093,"score_spread":0.24916379108636058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226263340","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15643474,0.00020135568,0.8427646,0.0000792719,0.000088836656,0.000104501756,0.00000874579,0.00013716806,0.00018072917],"genre_scores_gemma":[0.76142687,0.00000952852,0.23846768,0.000025187532,0.000013001243,0.000029365065,0.0000040391174,0.000010384412,0.000013956719],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986855,0.00011154698,0.00030862726,0.00031652622,0.00039105053,0.00018677714],"domain_scores_gemma":[0.99924225,0.00008333562,0.00026651783,0.00034425376,0.00003348078,0.00003018923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002069452,0.00010762409,0.00019040688,0.00010018491,0.00015187277,0.000052402356,0.0011303807,0.000021167061,0.000045274435],"category_scores_gemma":[0.000025835521,0.0001202544,0.000053142812,0.00023548602,0.00006768655,0.00087005395,0.00046221088,0.0001883399,0.0000013111448],"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":[0.000015438593,0.0002070544,0.0010082225,0.000052539617,0.000011416995,0.000008889134,0.0027731012,0.010347914,0.96129113,0.000039292892,0.00004751945,0.024197483],"study_design_scores_gemma":[0.0002546221,0.00021414031,0.00079006644,0.00006135785,0.000011732108,0.0000044467065,0.00014260173,0.099712275,0.8932338,0.0051730094,0.00021802727,0.00018395323],"about_ca_topic_score_codex":0.00012642307,"about_ca_topic_score_gemma":0.0000023420223,"teacher_disagreement_score":0.6049921,"about_ca_system_score_codex":0.00010173032,"about_ca_system_score_gemma":0.000031451887,"threshold_uncertainty_score":0.4903832},"labels":[],"label_agreement":null},{"id":"W4229867752","doi":"10.1002/9781119111771.ch11","title":"Changing the Sampling Structure of an Image","year":2019,"lang":"en","type":"other","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Bilinear interpolation; Upsampling; Bicubic interpolation; Stairstep interpolation; Algorithm; Lattice (music); Interpolation (computer graphics); Computer science; Mathematics; Sampling (signal processing); Polynomial; Image scaling; Computer vision; Image (mathematics); Image processing; Multivariate interpolation; Mathematical analysis; Filter (signal processing)","score_opus":0.013139245854762482,"score_gpt":0.29585394710215634,"score_spread":0.28271470124739384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229867752","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000018341223,0.00016566749,0.867671,0.000055039658,0.00012732996,0.00016050076,0.0000058219925,0.00070386234,0.13110891],"genre_scores_gemma":[0.00022489636,0.00000646148,0.8794501,0.00016073119,0.000088287095,0.0000020137713,0.0000026548223,0.000118489974,0.1199464],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991883,0.000021200749,0.000113409464,0.00030743936,0.00017677176,0.00019289355],"domain_scores_gemma":[0.99866956,0.000025888694,0.0002107319,0.0010329357,0.000040402247,0.000020464013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000783869,0.00016182024,0.0001829607,0.00020338941,0.00003640197,0.000091410155,0.0015899404,0.00010283822,0.00009641574],"category_scores_gemma":[0.000018335119,0.00010111907,0.000034209803,0.0002550615,0.000057144116,0.00026232874,0.00040490553,0.00016656322,0.000009588068],"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":[0.000006557954,0.00009002014,0.000011489574,0.0013125116,0.00013125034,0.000014902232,0.003450932,0.000024495377,0.21646848,0.14589365,0.1207427,0.51185304],"study_design_scores_gemma":[0.00052560924,0.00023302808,0.00001506131,0.0018268902,0.000051785042,0.000102097605,0.00042091077,0.07375588,0.22502992,0.13137664,0.5643879,0.0022742394],"about_ca_topic_score_codex":0.000014108397,"about_ca_topic_score_gemma":0.0000062235713,"teacher_disagreement_score":0.50957876,"about_ca_system_score_codex":0.00001130677,"about_ca_system_score_gemma":0.0000391117,"threshold_uncertainty_score":0.4123516},"labels":[],"label_agreement":null},{"id":"W4230576703","doi":"10.32920/ryerson.14657565.v1","title":"Large receptive field networks for accurate high-scale image super-resolution","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Convolutional neural network; Field (mathematics); Receptive field; Scale (ratio); Artificial intelligence; Image (mathematics); Network architecture; Pattern recognition (psychology); Superresolution; Mathematics; Cartography; Geography","score_opus":0.016989435362208042,"score_gpt":0.30224741273194805,"score_spread":0.28525797736974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230576703","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020048198,0.00047943497,0.9926545,0.0025175915,0.0010606383,0.0007235447,0.00002116472,0.001438438,0.0009042102],"genre_scores_gemma":[0.03337923,0.00021347645,0.96351033,0.0012482794,0.00027592923,0.0004586451,0.00014198686,0.000038668455,0.00073343696],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973478,0.00009703873,0.0004390882,0.0012375588,0.000244491,0.0006340272],"domain_scores_gemma":[0.9973774,0.00027333348,0.00025578716,0.0013201503,0.00066858716,0.00010474436],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044442137,0.00040041254,0.00046570261,0.00011367305,0.00023228904,0.00082744757,0.0016892118,0.00048309893,0.00005721424],"category_scores_gemma":[0.0002494841,0.00039643954,0.0002165803,0.00027782837,0.000051356816,0.0011738148,0.0036826532,0.0008164158,0.000006834323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040311352,0.0021486396,0.00026822515,0.00272739,0.0005885562,0.0002465808,0.0081992755,0.010118474,0.034156233,0.08788723,0.43465534,0.41860095],"study_design_scores_gemma":[0.00035131996,0.00012268903,0.000049540853,0.0003369827,0.000025391135,0.0000087432,0.000121834906,0.9137505,0.035666976,0.045808338,0.002976082,0.00078156625],"about_ca_topic_score_codex":0.00007777997,"about_ca_topic_score_gemma":0.00014509386,"teacher_disagreement_score":0.90363204,"about_ca_system_score_codex":0.00015714696,"about_ca_system_score_gemma":0.00020561085,"threshold_uncertainty_score":0.9998487},"labels":[],"label_agreement":null},{"id":"W4232295264","doi":"10.1109/cvprw50498.2020.00004","title":"CVPRW 2020 TOC","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Shenzhen Research Institute, City University of Hong Kong; University of Illinois at Urbana-Champaign; Shenzhen Research Institute of Big Data; Lomonosov Moscow State University; Technische Universität Dortmund; University of Chinese Academy of Sciences; Uppsala Universitet; University of California, San Diego; Oulun Yliopisto; Southern Federal University; Universidad de Zaragoza; University of Haifa; University of Central Florida; Eidgenössische Technische Hochschule Zürich; Florida State University; Chinese Academy of Sciences; Technische Universität Braunschweig; Microsoft Research Asia; SenseTime Group; Commonwealth Scientific and Industrial Research Organisation; Cardiff University; York University; RWTH Aachen University; Royal Bank of Canada; National Institute of Advanced Industrial Science and Technology; Bergische Universität Wuppertal; McGill University; Institut national de recherche en informatique et en automatique (INRIA); City University of Hong Kong; Google; University of Tsukuba; Indian Institute of Technology Gandhinagar; Peng Cheng Laboratory; Microsoft Research; Ford Motor Company","keywords":"Computer science","score_opus":0.017327919426459924,"score_gpt":0.2579761083198838,"score_spread":0.2406481888934239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232295264","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017505563,0.000065337015,0.9584149,0.018040387,0.000028166041,0.000047754726,1.4111525e-7,0.0015370927,0.02184869],"genre_scores_gemma":[0.05774973,0.000005658319,0.9337055,0.008206879,0.000047559184,0.0000058551095,2.0344068e-7,0.0000047759745,0.0002738177],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994457,0.000008520469,0.000083530984,0.00023447983,0.000107105196,0.00012068814],"domain_scores_gemma":[0.9996279,0.000014857498,0.000024459574,0.00021473582,0.000035671495,0.000082414335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000034005687,0.00006149675,0.00006490706,0.000010536302,0.00003760865,0.000093820134,0.0007191115,0.000017371327,0.000039260252],"category_scores_gemma":[0.00006331069,0.000053357897,0.000019645515,0.00027236558,0.000017866974,0.00058995094,0.0003102861,0.000070187416,0.00015785603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008025586,0.000049211783,0.00025830462,0.000048666574,0.000010163964,0.00010142989,0.0013103032,0.000013275291,0.05622517,0.30305946,0.20059444,0.43832156],"study_design_scores_gemma":[0.00025763927,0.00022884928,0.000112979265,0.000016660395,0.0000028490429,0.00002546715,0.00002301564,0.59299463,0.13989653,0.08831813,0.17762229,0.000500948],"about_ca_topic_score_codex":0.0000011667518,"about_ca_topic_score_gemma":1.882092e-7,"teacher_disagreement_score":0.59298134,"about_ca_system_score_codex":0.000007725562,"about_ca_system_score_gemma":0.000024448962,"threshold_uncertainty_score":0.21758719},"labels":[],"label_agreement":null},{"id":"W4233785726","doi":"10.32920/ryerson.14657565","title":"Large receptive field networks for accurate high-scale image super-resolution","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Convolutional neural network; Receptive field; Field (mathematics); Scale (ratio); Artificial intelligence; Image (mathematics); Network architecture; Superresolution; High resolution; Pattern recognition (psychology); Mathematics; Remote sensing; Cartography; Geography","score_opus":0.016989435362208042,"score_gpt":0.30224741273194805,"score_spread":0.28525797736974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233785726","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020048198,0.00047943497,0.9926545,0.0025175915,0.0010606383,0.0007235447,0.00002116472,0.001438438,0.0009042102],"genre_scores_gemma":[0.03337923,0.00021347645,0.96351033,0.0012482794,0.00027592923,0.0004586451,0.00014198686,0.000038668455,0.00073343696],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973478,0.00009703873,0.0004390882,0.0012375588,0.000244491,0.0006340272],"domain_scores_gemma":[0.9973774,0.00027333348,0.00025578716,0.0013201503,0.00066858716,0.00010474436],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044442137,0.00040041254,0.00046570261,0.00011367305,0.00023228904,0.00082744757,0.0016892118,0.00048309893,0.00005721424],"category_scores_gemma":[0.0002494841,0.00039643954,0.0002165803,0.00027782837,0.000051356816,0.0011738148,0.0036826532,0.0008164158,0.000006834323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040311352,0.0021486396,0.00026822515,0.00272739,0.0005885562,0.0002465808,0.0081992755,0.010118474,0.034156233,0.08788723,0.43465534,0.41860095],"study_design_scores_gemma":[0.00035131996,0.00012268903,0.000049540853,0.0003369827,0.000025391135,0.0000087432,0.000121834906,0.9137505,0.035666976,0.045808338,0.002976082,0.00078156625],"about_ca_topic_score_codex":0.00007777997,"about_ca_topic_score_gemma":0.00014509386,"teacher_disagreement_score":0.90363204,"about_ca_system_score_codex":0.00015714696,"about_ca_system_score_gemma":0.00020561085,"threshold_uncertainty_score":0.9998487},"labels":[],"label_agreement":null},{"id":"W4236827786","doi":"10.21275/v5i3.nov162307","title":"Literature Survey on Image Deblurring Techniques","year":2016,"lang":"en","type":"article","venue":"International Journal of Science and Research (IJSR)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Deblurring; Image (mathematics); Computer science; Computer vision; Artificial intelligence; Image processing; Image restoration","score_opus":0.054854227098366896,"score_gpt":0.4342673780056916,"score_spread":0.3794131509073247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236827786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059192408,0.000614691,0.9137297,0.0221029,0.000833263,0.00018668696,0.000015111277,0.00015795747,0.0031672402],"genre_scores_gemma":[0.76141334,0.00074748165,0.23710586,0.00027465765,0.00020655704,0.000003689971,1.9801047e-7,0.000007410801,0.00024081956],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99646,0.00012826407,0.00027158423,0.0002963635,0.0025177656,0.00032599538],"domain_scores_gemma":[0.99247295,0.00045569494,0.00015757154,0.00023372645,0.006512894,0.000167158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0078082187,0.000092084716,0.000111632435,0.001234679,0.00025283365,0.0010018222,0.0031098537,0.00004014927,0.000004478286],"category_scores_gemma":[0.004103563,0.000052046118,0.000032092743,0.00100882,0.0013578783,0.0038102267,0.0007143821,0.00038163876,0.000006358306],"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":[0.000060824088,0.00007965612,0.0014051318,0.0000055094597,0.000010630903,0.00022896452,0.00037068597,1.9494288e-7,0.528507,0.00787565,0.0029346012,0.45852116],"study_design_scores_gemma":[0.00052044087,0.00069336616,0.025723271,0.0023427405,0.000001339874,0.0007223834,0.000043668642,0.00092652236,0.8617102,0.09772764,0.009282377,0.0003060625],"about_ca_topic_score_codex":0.000009286413,"about_ca_topic_score_gemma":0.0000011487793,"teacher_disagreement_score":0.7022209,"about_ca_system_score_codex":0.00022853709,"about_ca_system_score_gemma":0.0004518798,"threshold_uncertainty_score":0.9660595},"labels":[],"label_agreement":null},{"id":"W4242963054","doi":"10.1109/icpr.2004.1334046","title":"Blind super-resolution using a learning-based approach","year":2004,"lang":"en","type":"article","venue":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Point spread function; Deconvolution; Superresolution; Artificial intelligence; Blind deconvolution; Computer science; Image restoration; Measure (data warehouse); Image resolution; Computer vision; Optical transfer function; Image (mathematics); Point (geometry); Function (biology); Iterative reconstruction; Pattern recognition (psychology); Image processing; Algorithm; Mathematics; Optics; Data mining; Physics","score_opus":0.07349998648637024,"score_gpt":0.30295193936066717,"score_spread":0.22945195287429693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242963054","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032771498,0.000051445255,0.9573346,0.0023972967,0.0003412682,0.00045881132,0.000027773227,0.00037252597,0.006244797],"genre_scores_gemma":[0.85122716,0.000011886893,0.14783752,0.00053718325,0.0001180029,0.00006402986,0.000022475675,0.00003247268,0.00014929143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99741465,0.00003218799,0.0005506195,0.0006936927,0.00091251894,0.0003963142],"domain_scores_gemma":[0.9972101,0.000032998527,0.0006244499,0.0002449055,0.0017879113,0.00009967902],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004898692,0.0003437395,0.0002692748,0.00039276775,0.00028228792,0.00034497227,0.0018587287,0.00015295105,0.000053486925],"category_scores_gemma":[0.00029898668,0.0002973938,0.0001614948,0.0005598009,0.00021818942,0.0011998441,0.000252193,0.0005403182,0.000034204164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017064366,0.012091737,0.047726896,0.0023972816,0.0012018629,0.000044471446,0.008741416,0.07721007,0.31875023,0.09160742,0.007095483,0.43142667],"study_design_scores_gemma":[0.0047498234,0.0005871724,0.0013887571,0.0025723374,0.00008267936,0.00018062723,0.00031611233,0.60138714,0.2357562,0.15106183,0.00049632316,0.0014210052],"about_ca_topic_score_codex":0.000082726314,"about_ca_topic_score_gemma":0.000003706154,"teacher_disagreement_score":0.81845564,"about_ca_system_score_codex":0.00037936424,"about_ca_system_score_gemma":0.00028228603,"threshold_uncertainty_score":0.99994785},"labels":[],"label_agreement":null},{"id":"W4251418376","doi":"10.1002/ima.20096","title":"User‐friendly image sharing using polynomials with different primes","year":2007,"lang":"en","type":"article","venue":"International Journal of Imaging Systems and Technology","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Image sharing; Computer science; Image (mathematics); Transmission (telecommunications); Interpolation (computer graphics); User Friendly; Shadow (psychology); Fault tolerance; Quality (philosophy); Scheme (mathematics); Lagrange polynomial; Theoretical computer science; Algorithm; Computer engineering; Distributed computing; Computer vision; Artificial intelligence; Mathematics; Programming language; Class (philosophy); Telecommunications","score_opus":0.008187354471279213,"score_gpt":0.2905698207498802,"score_spread":0.282382466278601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251418376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13078728,0.0011334041,0.8663827,0.0009695857,0.00041776692,0.00005996461,7.218551e-7,0.00014418727,0.00010435865],"genre_scores_gemma":[0.7025997,0.000017154938,0.29723167,0.000027858885,0.000092323484,0.0000010377955,1.347827e-7,0.000010347515,0.000019771684],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99871856,0.0000122462225,0.00049415557,0.00022438812,0.00032760596,0.00022301837],"domain_scores_gemma":[0.9983299,0.000048981943,0.00063762086,0.00020717779,0.00072063634,0.000055680186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045782907,0.00014454452,0.0002443817,0.00080570206,0.000071744354,0.0003288766,0.0011087627,0.000049653307,7.5065793e-7],"category_scores_gemma":[0.000081918435,0.00010845976,0.000030746207,0.00021027084,0.00014986965,0.0010295131,0.00036114824,0.00024087878,3.531987e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011703367,0.0002061255,0.14296393,0.00007118399,0.00036372233,0.002430511,0.00036976198,0.00003412747,0.6138349,0.11788185,0.00027674195,0.12145011],"study_design_scores_gemma":[0.0063812626,0.0011762556,0.010407176,0.006106499,0.00014968963,0.10252524,0.002072001,0.14280719,0.65569955,0.054227065,0.016233144,0.0022149035],"about_ca_topic_score_codex":0.00002265376,"about_ca_topic_score_gemma":0.000001634194,"teacher_disagreement_score":0.57181245,"about_ca_system_score_codex":0.00013607185,"about_ca_system_score_gemma":0.0000572213,"threshold_uncertainty_score":0.44228604},"labels":[],"label_agreement":null},{"id":"W4252258828","doi":"10.32920/ryerson.14660967.v1","title":"Fast and Efficient Edge Fusing Network Architectures for Accurate Single Image Super-resolution","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Convolution (computer science); Enhanced Data Rates for GSM Evolution; Field (mathematics); Pattern recognition (psychology); Image (mathematics); Computation; Network architecture; Feature (linguistics); Image resolution; Feature extraction; Computer vision; Artificial neural network; Algorithm; Mathematics","score_opus":0.024809781669430857,"score_gpt":0.2884053434853798,"score_spread":0.2635955618159489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252258828","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070934957,0.0019046733,0.9874454,0.0006873592,0.00055031246,0.0006702321,0.000008548306,0.0010546881,0.00058530015],"genre_scores_gemma":[0.11508546,0.000017437587,0.88412803,0.0002648945,0.00025172246,0.000110417655,0.000022514952,0.000036228008,0.00008328999],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997456,0.00009010657,0.0003980369,0.0011969175,0.00025462385,0.0006042776],"domain_scores_gemma":[0.998214,0.00019752828,0.00023413241,0.0008952356,0.0003459194,0.00011317594],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00039548468,0.00041426113,0.00041915325,0.00014101552,0.0003461747,0.001386853,0.00091646204,0.00020976504,0.000004309375],"category_scores_gemma":[0.00020519597,0.00039265532,0.00013476143,0.0002697105,0.00016526751,0.00016188846,0.0035513283,0.00048506848,9.48198e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007960147,0.00050923397,0.000061890234,0.0019959852,0.000120158,0.000077345234,0.003958087,0.3187454,0.106981985,0.004657486,0.0033916614,0.5594212],"study_design_scores_gemma":[0.00017888965,0.00006076652,0.000079502715,0.0004964302,0.00002000137,0.00004088442,0.000030474466,0.95892984,0.01949729,0.019799566,0.00031886497,0.0005474881],"about_ca_topic_score_codex":0.00002034708,"about_ca_topic_score_gemma":0.000018485945,"teacher_disagreement_score":0.64018446,"about_ca_system_score_codex":0.00012289039,"about_ca_system_score_gemma":0.00018719913,"threshold_uncertainty_score":0.99985254},"labels":[],"label_agreement":null},{"id":"W4281481157","doi":"10.1016/j.jag.2022.102826","title":"Super-resolving and composing building dataset using a momentum spatial-channel attention residual feature aggregation network","year":2022,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council; University of Waterloo; Central University of Finance and Economics","keywords":"Generalizability theory; Residual; Image resolution; Computer science; Artificial intelligence; Generalization; Channel (broadcasting); Feature (linguistics); Mean squared error; Pattern recognition (psychology); Machine learning; Algorithm; Data mining; Statistics; Mathematics; Telecommunications","score_opus":0.017944467005219615,"score_gpt":0.26252368602325926,"score_spread":0.24457921901803964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281481157","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12824284,0.00012347562,0.8696752,0.001353696,0.00035856856,0.00014345607,0.000025671847,0.000045894903,0.000031152937],"genre_scores_gemma":[0.58355963,0.00006106084,0.41516754,0.0007178242,0.00020115919,0.0000070175565,0.00026870795,0.000008907214,0.0000081316575],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985657,0.000036726185,0.0004792289,0.00014721297,0.00062560075,0.00014554856],"domain_scores_gemma":[0.99871963,0.000058621026,0.0007689767,0.0001092709,0.00029161404,0.00005187919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071926723,0.000120598306,0.00013621178,0.00026598398,0.00041793115,0.00041227718,0.00032046274,0.000037419653,0.0000042721663],"category_scores_gemma":[0.000040333358,0.00012875171,0.000024703899,0.00021304254,0.000028534621,0.0027987445,0.00034809927,0.00025541888,3.3614938e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089112093,0.00020665686,0.0042454307,0.00020252001,0.00031278713,0.000051718725,0.008469972,0.32849848,0.09480491,0.05032077,0.0060682516,0.5059274],"study_design_scores_gemma":[0.0008365078,0.00008539884,0.005738407,0.000112373964,0.000016550213,0.00028712148,0.00025858925,0.97438204,0.0014054123,0.011093663,0.005587756,0.00019617057],"about_ca_topic_score_codex":0.000016459428,"about_ca_topic_score_gemma":0.000003386765,"teacher_disagreement_score":0.64588356,"about_ca_system_score_codex":0.00009427373,"about_ca_system_score_gemma":0.000056558954,"threshold_uncertainty_score":0.52503425},"labels":[],"label_agreement":null},{"id":"W4281756502","doi":"10.5194/isprs-archives-xliii-b1-2022-31-2022","title":"IMPACT OF DEEP LEARNING-BASED SUPER-RESOLUTION ON BUILDING FOOTPRINT EXTRACTION","year":2022,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Footprint; Computer science; Artificial intelligence; Residual; Image resolution; Intersection (aeronautics); Deep learning; Feature extraction; Interpolation (computer graphics); Data mining; Pattern recognition (psychology); Remote sensing; Computer vision; Image (mathematics); Geography; Cartography; Algorithm","score_opus":0.01631473084434878,"score_gpt":0.2881626038045566,"score_spread":0.27184787296020785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281756502","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012170712,0.000027134265,0.98150283,0.0021568842,0.0010857484,0.0005453,0.000041648407,0.00009310616,0.002376611],"genre_scores_gemma":[0.97205657,0.0000481499,0.027330332,0.0004422806,0.000063869324,8.5553796e-7,0.000020738402,0.000011083225,0.000026130165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99501896,0.00046857397,0.0013420249,0.0004557011,0.002225345,0.0004894086],"domain_scores_gemma":[0.9956383,0.0011570288,0.002159043,0.00056215085,0.00035969188,0.00012380249],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0017761857,0.00039362838,0.000398387,0.0012953057,0.0019222231,0.00063304463,0.002475077,0.00006583072,0.0000066168495],"category_scores_gemma":[0.0009011414,0.00026399625,0.000465519,0.0013323029,0.0019933407,0.00063212565,0.0012980818,0.000678383,9.2284034e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017310896,0.00003702322,0.0002396414,0.000026015774,0.000053826145,3.021277e-7,0.0018446624,0.064352706,0.009328995,0.000021364267,0.000009692398,0.92391264],"study_design_scores_gemma":[0.0005961607,0.00045886185,0.0037153685,0.00019925553,0.000022510087,0.0001174743,0.0006690077,0.9689986,0.015748614,0.008142364,0.0010546999,0.00027704367],"about_ca_topic_score_codex":0.5715911,"about_ca_topic_score_gemma":0.027285544,"teacher_disagreement_score":0.95988584,"about_ca_system_score_codex":0.00013406062,"about_ca_system_score_gemma":0.00031990057,"threshold_uncertainty_score":0.9999812},"labels":[],"label_agreement":null},{"id":"W4282937861","doi":"10.1038/s41598-022-13658-4","title":"A new generative adversarial network for medical images super resolution","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":175,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Qatar National Library","keywords":"Computer science; Artificial intelligence; Deep learning; Image (mathematics); Convolutional neural network; Feature (linguistics); Computer vision; Image resolution; Scale (ratio); Pattern recognition (psychology); Network architecture; Path (computing); Cartography; Geography","score_opus":0.013876298408325367,"score_gpt":0.28271716588916745,"score_spread":0.26884086748084207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282937861","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015540725,0.00039021616,0.9867151,0.0018593854,0.009614367,0.00037879776,0.0000022510849,0.00045911327,0.00042535548],"genre_scores_gemma":[0.009589029,0.0000017956008,0.9844861,0.0002778505,0.0004899663,0.00018387774,0.000037045844,0.000014655186,0.0049196593],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99693865,0.00009636917,0.00042401813,0.0009946292,0.0011081279,0.00043821856],"domain_scores_gemma":[0.998434,0.00007051325,0.00024010785,0.0008935966,0.00018263482,0.00017915947],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0028897526,0.00013126066,0.00016265937,0.00011678393,0.001387384,0.0004338801,0.0007677768,0.000044714958,0.0001507872],"category_scores_gemma":[0.0005269465,0.00012929123,0.00009331912,0.00081921404,0.00014513296,0.00074470247,0.00090217026,0.00020163028,0.000002862047],"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":[0.000015698464,0.000042821117,0.00008131485,0.000007289992,0.0000096037365,0.00027020153,0.00045086656,0.0012020051,0.0063684774,0.00497998,0.9581545,0.02841726],"study_design_scores_gemma":[0.00026522143,0.00010891033,0.00002276854,0.000020233912,0.000009053342,0.00059975736,0.000025942229,0.091174535,0.010563679,0.42879653,0.46810928,0.00030406888],"about_ca_topic_score_codex":0.00002139431,"about_ca_topic_score_gemma":0.000007300514,"teacher_disagreement_score":0.4900452,"about_ca_system_score_codex":0.00014350416,"about_ca_system_score_gemma":0.0011076119,"threshold_uncertainty_score":0.9999127},"labels":[],"label_agreement":null},{"id":"W4283687192","doi":"10.1109/tip.2022.3184819","title":"Data Acquisition and Preparation for Dual-Reference Deep Learning of Image Super-Resolution","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Upsampling; Computer science; Artificial intelligence; Computer vision; Bicubic interpolation; Image resolution; Process (computing); Pixel; Image quality; Image (mathematics); Pattern recognition (psychology)","score_opus":0.03354670155997822,"score_gpt":0.32255401518992116,"score_spread":0.2890073136299429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283687192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014026625,0.0003283586,0.9969509,0.00023503094,0.0000844106,0.0003630451,0.00007037749,0.00045319775,0.00011197037],"genre_scores_gemma":[0.50582945,0.00002230209,0.49385333,0.000040969535,0.000012052216,0.00014661021,0.00003000846,0.00001665045,0.000048611117],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982308,0.00011446148,0.00036121486,0.00068229466,0.0003455788,0.00026563948],"domain_scores_gemma":[0.99877083,0.00011801509,0.00025422985,0.00053135445,0.00027291258,0.00005267488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056741876,0.00018331174,0.00019643038,0.00024503615,0.0011456929,0.00025102543,0.00060557836,0.000047272253,0.000015107794],"category_scores_gemma":[0.00003552676,0.00020929397,0.00003590637,0.00047585013,0.00013006305,0.0037387488,0.00004790699,0.000347042,0.0000013520452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002187941,0.0003318055,0.0000046790874,0.00043898245,0.000019372832,0.000004921118,0.0018173357,0.005259841,0.5583039,0.00024589163,0.00009137922,0.4332631],"study_design_scores_gemma":[0.00039058147,0.00033107586,0.000015097544,0.000061839266,0.000030443824,0.00005187203,0.000187069,0.91168785,0.08430857,0.0022644375,0.00043959674,0.00023154951],"about_ca_topic_score_codex":0.000009084404,"about_ca_topic_score_gemma":0.0000038034977,"teacher_disagreement_score":0.90642804,"about_ca_system_score_codex":0.000097390955,"about_ca_system_score_gemma":0.0001153596,"threshold_uncertainty_score":0.88118595},"labels":[],"label_agreement":null},{"id":"W4283736551","doi":"10.1007/s10618-022-00843-2","title":"VPint: value propagation-based spatial interpolation","year":2022,"lang":"en","type":"article","venue":"Data Mining and Knowledge Discovery","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Horizon 2020; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Interpolation (computer graphics); Multivariate interpolation; Value (mathematics); Computer science; Mathematics; Algorithm; Bilinear interpolation; Artificial intelligence; Statistics","score_opus":0.031453118059549263,"score_gpt":0.30222898437326057,"score_spread":0.2707758663137113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283736551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030278396,0.00044012172,0.9948609,0.00021866708,0.00026870464,0.00010626041,0.00010046751,0.00024504884,0.00073202443],"genre_scores_gemma":[0.711107,0.0000033680537,0.28801474,0.00012853606,0.00006590472,0.00004270327,0.00041735152,0.000012012586,0.00020834862],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889535,0.00010143479,0.00017800218,0.0005087827,0.00015947591,0.0001569601],"domain_scores_gemma":[0.99892414,0.00011272466,0.000105696294,0.0007837734,0.000037994483,0.000035654964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004148,0.000112717134,0.00010988914,0.000121562465,0.0003787974,0.0003134501,0.0010763846,0.000019149393,0.0000065306704],"category_scores_gemma":[0.00015806252,0.0001142003,0.000016083286,0.00025920366,0.000056406516,0.0018972747,0.0019414366,0.00012489634,0.0000027348908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008001284,0.0004896148,0.003747246,0.00019998054,0.00003277214,0.000028067449,0.00499597,0.00012762182,0.007099197,0.013998175,0.012438633,0.95676273],"study_design_scores_gemma":[0.00020362115,0.00009069417,0.00024100525,0.000047583122,0.000006815709,0.000009643997,0.00013223068,0.9928955,0.00077554386,0.0009839879,0.0044317567,0.00018164108],"about_ca_topic_score_codex":0.00003122918,"about_ca_topic_score_gemma":0.0000145372405,"teacher_disagreement_score":0.9927679,"about_ca_system_score_codex":0.00004326618,"about_ca_system_score_gemma":0.0002243084,"threshold_uncertainty_score":0.4656953},"labels":[],"label_agreement":null},{"id":"W4285102686","doi":"10.1109/dslw53931.2022.9820183","title":"Reaching a Better Trade-Off Between Image Quality and Attack Success Rates in Transfer-Based Adversarial Attacks","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Adversarial system; Computer science; Quality (philosophy); Image quality; Image (mathematics); Artificial intelligence; Transfer (computing); Computer vision; Parallel computing","score_opus":0.04893888661805306,"score_gpt":0.3551953639165813,"score_spread":0.30625647729852823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285102686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17408678,0.000071450966,0.81832886,0.006297561,0.00005807537,0.0002441327,0.000015529253,0.00048718604,0.00041041375],"genre_scores_gemma":[0.69231164,0.000002331054,0.30641484,0.0011350211,0.00003686232,0.000055874272,0.000011013918,0.00001645249,0.000015937614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975487,0.00045614832,0.00048128533,0.0007042637,0.00040703246,0.00040256637],"domain_scores_gemma":[0.99896425,0.00036768988,0.000074595075,0.00047852116,0.000025197345,0.0000897353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012194422,0.00023099533,0.00033801454,0.00021196491,0.0003232866,0.00023763717,0.0010078344,0.000056634726,0.000032872562],"category_scores_gemma":[0.000057846493,0.00023396061,0.00006561166,0.0005081821,0.000117405405,0.0013809815,0.00036959848,0.0005713076,0.0000012903614],"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":[0.0003334747,0.0008847597,0.33057442,0.0005259122,0.00008347748,0.000289172,0.009023169,0.00048006268,0.08020634,0.005308996,0.0024473646,0.5698428],"study_design_scores_gemma":[0.014040735,0.001513368,0.26661435,0.00027478326,0.00009379939,0.00006887871,0.0006715352,0.45713028,0.18704283,0.052288417,0.015136048,0.005124975],"about_ca_topic_score_codex":0.00014352363,"about_ca_topic_score_gemma":0.00002997089,"teacher_disagreement_score":0.5647179,"about_ca_system_score_codex":0.00013789276,"about_ca_system_score_gemma":0.00009936532,"threshold_uncertainty_score":0.9540637},"labels":[],"label_agreement":null},{"id":"W4285262681","doi":"10.1109/access.2022.3176441","title":"Frequency-Based Enhancement Network for Efficient Super-Resolution","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Minnow Environmental (Canada)","funders":"Agencia Estatal de Investigación; European Regional Development Fund; Ministerio de Economía y Competitividad","keywords":"Computer science; Memory footprint; Block (permutation group theory); Convolutional neural network; Discriminative model; Inference; Code (set theory); Computation; Algorithm; Footprint; Image (mathematics); Artificial intelligence; Computer engineering","score_opus":0.03173488534341811,"score_gpt":0.3210140793898929,"score_spread":0.2892791940464748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285262681","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035857456,0.00027995734,0.99326104,0.00075564726,0.0009405428,0.00050668354,0.000010378956,0.00051055127,0.00014943395],"genre_scores_gemma":[0.38095152,0.0000016400991,0.61686367,0.0010913468,0.00010038613,0.0009270117,0.0000102176045,0.000013936036,0.000040276005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844474,0.00006292325,0.0002329508,0.00047277627,0.00036470927,0.00042188697],"domain_scores_gemma":[0.99906087,0.00008087821,0.00013154268,0.0005533492,0.00012030772,0.00005308249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045619853,0.00013898636,0.00013643218,0.00008801485,0.0006454526,0.00020980556,0.0019244549,0.000024981555,0.00002774411],"category_scores_gemma":[0.00002960827,0.00014728693,0.00006431094,0.0005825167,0.000038250044,0.0004346975,0.00038510826,0.00014275462,0.0000036874471],"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":[0.00011350347,0.0010675788,0.0010765305,0.00022945364,0.00004304942,0.00003934869,0.0004889357,0.7378841,0.06052291,0.04631215,0.082699,0.06952344],"study_design_scores_gemma":[0.000351472,0.00016436644,0.000043826687,0.000022186105,0.00000633124,0.0000023490636,0.000003818977,0.90339583,0.059551317,0.028344117,0.007842026,0.0002723814],"about_ca_topic_score_codex":0.000018475774,"about_ca_topic_score_gemma":0.00000387438,"teacher_disagreement_score":0.37736577,"about_ca_system_score_codex":0.00024082784,"about_ca_system_score_gemma":0.00016738499,"threshold_uncertainty_score":0.6006187},"labels":[],"label_agreement":null},{"id":"W4285697890","doi":"10.20944/preprints202003.0313.v1","title":"Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network","year":2020,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Energy; Athabasca University; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Detector; Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Residual; Context (archaeology); Computer vision; Object detection; Overhead (engineering); Image resolution; Pattern recognition (psychology); Telecommunications; Algorithm; Geography","score_opus":0.05712182583916333,"score_gpt":0.3082877557819736,"score_spread":0.2511659299428103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285697890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1716924,0.00023154498,0.823715,0.00041240428,0.00031688545,0.0010743487,0.000003225981,0.001496422,0.0010578178],"genre_scores_gemma":[0.62397,0.00008780181,0.37539998,0.00022654692,0.00015817603,0.000027487626,0.0000027710444,0.00007290278,0.00005435321],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9946263,0.00037702778,0.00074991665,0.0028467032,0.0005062278,0.0008938157],"domain_scores_gemma":[0.99656296,0.00023129887,0.00058524543,0.002040605,0.00030220358,0.00027771134],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010233095,0.00081048487,0.0009282608,0.00042928947,0.0002605484,0.0003143884,0.0014938024,0.0003732877,0.000010726193],"category_scores_gemma":[0.0007290267,0.00084520684,0.00012695398,0.0011490239,0.00019439013,0.0005112548,0.004586654,0.0019328338,0.0000550093],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031100822,0.00004147763,0.003447966,0.0006007467,0.00010895302,0.0002494792,0.0031028956,0.0036666896,0.40769765,0.000021637265,0.000009765554,0.5807417],"study_design_scores_gemma":[0.00043554936,0.00014362128,0.029550884,0.001655224,0.000042137508,0.0001009544,0.00003337047,0.055944752,0.8962991,0.0143862665,0.00020868072,0.0011994614],"about_ca_topic_score_codex":0.00040118484,"about_ca_topic_score_gemma":0.00037941724,"teacher_disagreement_score":0.5795423,"about_ca_system_score_codex":0.0003811974,"about_ca_system_score_gemma":0.00034307965,"threshold_uncertainty_score":0.9993999},"labels":[],"label_agreement":null},{"id":"W4286239565","doi":"10.48550/arxiv.2207.08689","title":"Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelity","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fidelity; Weighting; Computer science; Image quality; Image (mathematics); Artificial intelligence; Quality (philosophy); Contrast (vision); Resolution (logic); Algorithm; Pattern recognition (psychology); Machine learning; Data mining; Computer vision","score_opus":0.08541980073831809,"score_gpt":0.28981537552339354,"score_spread":0.20439557478507545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286239565","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030168183,0.000049168266,0.96784925,0.000048482667,0.000152097,0.00021574866,0.00011420453,0.00029044424,0.0011124033],"genre_scores_gemma":[0.6488971,0.000040236093,0.35093206,0.000030857474,0.00001110181,0.0000020948992,0.000016260265,0.000009103952,0.00006118159],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976452,0.00038530192,0.00039053668,0.0010934691,0.00018958698,0.00029593322],"domain_scores_gemma":[0.9977416,0.0003868247,0.0003851448,0.0011361847,0.00021827502,0.00013200466],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00076975557,0.00026598352,0.00046309558,0.00015514337,0.00022284666,0.00010082246,0.001242473,0.000119846205,0.000053157357],"category_scores_gemma":[0.00021596524,0.0003289903,0.00008839668,0.0003034332,0.00033513253,0.00044921212,0.004541342,0.0006799419,0.0000011998706],"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":[0.00008036323,0.00054760685,0.023902602,0.0021138508,0.00013900499,0.0011705802,0.0005847516,0.016047152,0.0032263834,0.94407356,0.00043553227,0.007678645],"study_design_scores_gemma":[0.0002395505,0.00008120688,0.022331433,0.000063769025,0.000035702047,0.000008628649,0.000063256266,0.8030534,0.00006761108,0.1735742,0.00009725065,0.0003839935],"about_ca_topic_score_codex":0.00012997304,"about_ca_topic_score_gemma":0.000010686792,"teacher_disagreement_score":0.78700626,"about_ca_system_score_codex":0.00035990786,"about_ca_system_score_gemma":0.00042804575,"threshold_uncertainty_score":0.9999162},"labels":[],"label_agreement":null},{"id":"W4288075321","doi":"10.18280/ts.390333","title":"A Positive-Unlabeled Generative Adversarial Network for Super-Resolution Image Reconstruction Using a Charbonnier Loss","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Zhejiang Province; National Social Science Fund of China; Natural Science Foundation of Shandong Province","keywords":"Discriminator; Artificial intelligence; Benchmark (surveying); Outlier; Computer science; Pattern recognition (psychology); Generative adversarial network; Image (mathematics); Similarity (geometry); Resolution (logic); Process (computing); Superresolution; Algorithm; Telecommunications","score_opus":0.01964329475485243,"score_gpt":0.26365047528741725,"score_spread":0.24400718053256482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288075321","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018566342,0.00009007389,0.979179,0.0005627367,0.0004511577,0.0007480153,0.000047950158,0.00030150887,0.000053209784],"genre_scores_gemma":[0.1837637,0.0000027057063,0.81507146,0.00034468118,0.00046334715,0.000280511,0.000029284007,0.000020403662,0.000023901322],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981641,0.0001793661,0.00033413948,0.0005371097,0.00034140627,0.00044386092],"domain_scores_gemma":[0.9992811,0.00006487679,0.0002029314,0.0002082739,0.00017735455,0.00006546876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005350368,0.00020446235,0.00020780934,0.00011729552,0.0010930477,0.00014578467,0.00045570853,0.000039293784,0.000085908396],"category_scores_gemma":[0.000019479503,0.00022879367,0.00010087975,0.00040429246,0.00011435382,0.001022931,0.00027932564,0.00019138015,0.0000013096725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009377101,0.00050654454,0.00024937076,0.00006282702,0.00020752515,0.00006776735,0.003556715,0.026179127,0.84660983,0.0329157,0.0031678157,0.08553907],"study_design_scores_gemma":[0.0012854681,0.00041115575,0.00005785024,0.000029991828,0.000030217723,0.0001153204,0.000054071264,0.96912134,0.012821158,0.014999937,0.00074635807,0.00032713276],"about_ca_topic_score_codex":0.000017663442,"about_ca_topic_score_gemma":0.0000021021654,"teacher_disagreement_score":0.9429422,"about_ca_system_score_codex":0.0004164438,"about_ca_system_score_gemma":0.00015790404,"threshold_uncertainty_score":0.93299353},"labels":[],"label_agreement":null},{"id":"W4290973806","doi":"10.1109/icc45855.2022.9838913","title":"An Adaptive High-Fidelity Image Compression Framework for Internet of Vehicles","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Image compression; Computer science; Data compression; Data compression ratio; Image quality; Pixel; Bit rate; High fidelity; Fidelity; Algorithm; Image (mathematics); Compression ratio; Distortion (music); Artificial intelligence; Computer vision; Image processing; Computer engineering; Telecommunications; Bandwidth (computing); Engineering","score_opus":0.11771470430098464,"score_gpt":0.4077652798176,"score_spread":0.2900505755166154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290973806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021381187,0.00006436712,0.9912012,0.0038571022,0.00038264127,0.00032306134,0.00022445765,0.0002574429,0.0015515786],"genre_scores_gemma":[0.5316729,0.00003527499,0.4675982,0.00023132467,0.000016708524,0.00031348004,0.00007003706,0.000010010586,0.000052060623],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816954,0.00029894843,0.00044573593,0.00041577144,0.00049520924,0.00017478019],"domain_scores_gemma":[0.9963417,0.0005754346,0.00045685767,0.0018957545,0.0006656475,0.0000646202],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.00046016826,0.00016410487,0.00020632194,0.00021339496,0.00038054504,0.00014476293,0.0067351386,0.000049992545,0.00014130225],"category_scores_gemma":[0.0001530649,0.00018102814,0.00008166522,0.0002639696,0.00022570697,0.0007744098,0.0014889203,0.00057657994,0.0000056375516],"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":[0.00007887382,0.0004641476,0.0000356059,0.0000058264077,0.000027154301,8.383366e-7,0.00053412037,0.00009882681,0.03304599,0.94963884,0.0009279192,0.015141872],"study_design_scores_gemma":[0.00020435106,0.0003928135,0.0002094708,0.00008054335,0.000005996028,0.0000037763255,0.00021921113,0.5333127,0.011763405,0.45218825,0.0014398731,0.00017963296],"about_ca_topic_score_codex":0.00010580653,"about_ca_topic_score_gemma":0.000014096059,"teacher_disagreement_score":0.53321385,"about_ca_system_score_codex":0.00017941392,"about_ca_system_score_gemma":0.00014454515,"threshold_uncertainty_score":0.9986389},"labels":[],"label_agreement":null},{"id":"W4293510502","doi":"10.1155/2022/9393589","title":"Rendered Image Superresolution Reconstruction with Multichannel Feature Network","year":2022,"lang":"en","type":"article","venue":"Scientific Programming","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Department of Science and Technology of Jilin Province; Education Department of Jilin Province; People's Government of Jilin Province","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Process (computing); Computer vision; Residual; Moment (physics); Superresolution; Image (mathematics); Enhanced Data Rates for GSM Evolution; Production (economics); Pattern recognition (psychology); Algorithm","score_opus":0.011684432901995005,"score_gpt":0.23676571223544576,"score_spread":0.22508127933345076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293510502","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014257781,0.0004865045,0.9941932,0.00055840233,0.0013281034,0.00039853927,0.0000029543232,0.0013495603,0.0002569324],"genre_scores_gemma":[0.097290896,0.0000013029376,0.90161604,0.000040389546,0.00007011556,0.0002129134,0.000019049929,0.00001731381,0.00073196803],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997681,0.00011342978,0.0001820459,0.0008431465,0.000576765,0.00060356245],"domain_scores_gemma":[0.9988226,0.000027061275,0.00016870639,0.00070736936,0.00018353788,0.00009069071],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0010027172,0.00016983614,0.00014025084,0.00018321694,0.0022446858,0.0009876456,0.0009483927,0.00003629641,0.000011617348],"category_scores_gemma":[0.000053379314,0.00016076992,0.000049935825,0.0019867832,0.00029629114,0.0014213116,0.00047811333,0.00039680273,0.0000055825412],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034780074,0.0001146285,0.00022206023,0.00003518702,0.000017369595,0.00004911464,0.0013668787,0.00081034406,0.012952116,0.0028909105,0.005156267,0.97635037],"study_design_scores_gemma":[0.0012216817,0.00059709273,0.00022710486,0.00021009489,0.000038760885,0.0013513747,0.001546502,0.75673234,0.010909728,0.029036619,0.19669576,0.001432925],"about_ca_topic_score_codex":0.0000074458203,"about_ca_topic_score_gemma":0.0000121006005,"teacher_disagreement_score":0.9749174,"about_ca_system_score_codex":0.00018989584,"about_ca_system_score_gemma":0.00014175914,"threshold_uncertainty_score":0.99905425},"labels":[],"label_agreement":null},{"id":"W4293863524","doi":"10.1109/siu55565.2022.9864797","title":"Transfer Learning Based Super Resolution of Aerial Images","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Transfer of learning; Mean squared error; Pattern recognition (psychology); Generative model; Generative grammar; Perception; Image (mathematics); Object (grammar); Computer vision; Machine learning; Mathematics","score_opus":0.027225087207898638,"score_gpt":0.2821244282248613,"score_spread":0.25489934101696266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293863524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00058167946,0.0022284482,0.9928006,0.002297566,0.000010864035,0.00038223126,0.000023321834,0.00042202,0.0012532568],"genre_scores_gemma":[0.75688714,0.0001446395,0.2413554,0.00013060191,0.000012528181,0.001275433,0.000060418468,0.000016364982,0.00011744921],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814916,0.00030933437,0.00045308715,0.00047078574,0.000362606,0.00025500427],"domain_scores_gemma":[0.99796987,0.0001744219,0.00019651889,0.0011709015,0.00041142068,0.00007684211],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00060752983,0.00019002026,0.00023651442,0.00024771978,0.001979226,0.00021140117,0.002205554,0.000052848583,0.000064344276],"category_scores_gemma":[0.000023822584,0.00021528768,0.000055148495,0.0010609787,0.00043414137,0.0007823129,0.0008717405,0.0006232644,0.0000023149655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052643645,0.00073196564,0.00030092313,0.0002182044,0.000026275942,0.0000010545646,0.0019826724,0.0028875794,0.17909569,0.124560185,0.0002426457,0.68990016],"study_design_scores_gemma":[0.0005810607,0.0002160682,0.0001378031,0.00008116482,0.000045017823,0.000022795088,0.0010272993,0.91773874,0.012185565,0.022929285,0.04450798,0.000527217],"about_ca_topic_score_codex":0.000031352814,"about_ca_topic_score_gemma":0.0000032674998,"teacher_disagreement_score":0.9148512,"about_ca_system_score_codex":0.00007150353,"about_ca_system_score_gemma":0.00048689786,"threshold_uncertainty_score":0.9993201},"labels":[],"label_agreement":null},{"id":"W4296046318","doi":"10.1093/pasj/psac071","title":"Deblurring galaxy images with Tikhonov regularization on magnitude domain","year":2022,"lang":"en","type":"article","venue":"Publications of the Astronomical Society of Japan","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Planetary Science Division; Japan Society for the Promotion of Science; Science Mission Directorate; Smithsonian Astrophysical Observatory; University of Edinburgh; Max-Planck-Institut für Astronomie; National Astronomical Observatory of Japan; National Central University; Ministry of Education, Culture, Sports, Science and Technology; Queen's University; Cabinet Office, Government of Japan; Eötvös Loránd Tudományegyetem; Academia Sinica; Space Telescope Science Institute; Sumitomo Foundation; Los Alamos National Laboratory; Johns Hopkins University; Princeton University; Toray Science Foundation; High Energy Accelerator Research Organization; University of Tokyo; Queen's University Belfast; Japan Science and Technology Agency; Smithsonian Institution; Durham University; University of Maryland; National Aeronautics and Space Administration; National Science Foundation","keywords":"Deblurring; Physics; Tikhonov regularization; Galaxy; Regularization (linguistics); Magnitude (astronomy); Astrophysics; Astronomy; Mathematical analysis; Artificial intelligence; Image (mathematics); Image processing; Inverse problem; Image restoration; Mathematics; Computer science","score_opus":0.00800065723233432,"score_gpt":0.2245882153646086,"score_spread":0.2165875581322743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296046318","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13250715,0.000019326999,0.8549976,0.011740752,0.000025440904,0.0002354771,0.000009922658,0.00014403278,0.00032030288],"genre_scores_gemma":[0.46724346,4.4733162e-7,0.5324961,0.000045987636,0.000008352473,0.00006492144,0.0000046798355,0.000007239291,0.00012883783],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9990283,0.000056526205,0.0002329762,0.00026397288,0.00025235483,0.00016586162],"domain_scores_gemma":[0.9986525,0.00006167965,0.000318862,0.0008121008,0.00011766499,0.000037174414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028243175,0.00009657247,0.00013003394,0.000047961556,0.00032032238,0.000051487987,0.0016237663,0.000021134962,0.000013919685],"category_scores_gemma":[0.000034814155,0.00007897166,0.00011512905,0.00050292874,0.00021038689,0.00041032734,0.0007755173,0.00019389504,8.082803e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105069346,0.0022959341,0.35429227,0.00017227589,0.0003756226,1.3170318e-8,0.0048562987,0.049820203,0.11488815,0.3854958,0.028029505,0.059668846],"study_design_scores_gemma":[0.0020970434,0.0009288962,0.5847646,0.00014431827,0.000072737676,0.000014249611,0.0014336306,0.15124953,0.15102437,0.09600042,0.011229243,0.0010409617],"about_ca_topic_score_codex":0.000008028726,"about_ca_topic_score_gemma":3.0349878e-7,"teacher_disagreement_score":0.33473632,"about_ca_system_score_codex":0.000135701,"about_ca_system_score_gemma":0.00011359293,"threshold_uncertainty_score":0.32203707},"labels":[],"label_agreement":null},{"id":"W4304084118","doi":"10.1145/3503161.3547899","title":"Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelity","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 30th ACM International Conference on Multimedia","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Fidelity; Weighting; Computer science; Image quality; Image (mathematics); Artificial intelligence; Image resolution; Quality (philosophy); Resolution (logic); Contrast (vision); Algorithm; Pattern recognition (psychology); Machine learning; Computer vision; Data mining","score_opus":0.055176069640440195,"score_gpt":0.3771203647401609,"score_spread":0.3219442950997207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304084118","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19273084,0.000049671617,0.78377366,0.007579996,0.0010904297,0.0008569297,0.00044859416,0.00032655112,0.013143329],"genre_scores_gemma":[0.6069357,0.00000534984,0.3928917,0.00007779817,0.00001553001,0.000035309553,0.0000031528543,0.000004715244,0.000030774034],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808216,0.00003900611,0.00047532722,0.00037867288,0.00085016777,0.00017464219],"domain_scores_gemma":[0.998213,0.0002981295,0.00045977312,0.00027214637,0.0006963107,0.000060666647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007987108,0.00014232959,0.00022728753,0.00009762014,0.00016527936,0.000088577486,0.002099974,0.000028954713,0.00007318544],"category_scores_gemma":[0.0019406484,0.00012088649,0.00004821497,0.000159858,0.00027283657,0.0004168678,0.0020109683,0.0003230421,6.230358e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000128883,0.0004884135,0.024927521,0.00028226603,0.00006388808,0.0000039964384,0.0015795591,0.00002755604,0.35433242,0.5729509,0.0010034173,0.04421119],"study_design_scores_gemma":[0.0005570368,0.00022735588,0.08447376,0.00011045637,0.000012021825,0.000024720497,0.00031163415,0.7762513,0.010068252,0.12757201,0.00013461657,0.00025682163],"about_ca_topic_score_codex":0.000025181536,"about_ca_topic_score_gemma":9.807659e-7,"teacher_disagreement_score":0.7762238,"about_ca_system_score_codex":0.00013424094,"about_ca_system_score_gemma":0.00015417422,"threshold_uncertainty_score":0.4929608},"labels":[],"label_agreement":null},{"id":"W4307345599","doi":"10.3390/app122110871","title":"Research on Key Technologies of Super-Resolution Reconstruction of Medium and Long Wave Maritime Infrared Image","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Department of Science and Technology of Jilin Province","keywords":"Computer science; Block (permutation group theory); Artificial intelligence; Infrared; Feature (linguistics); Remote sensing; Computer vision; Feature extraction; Optics; Geology; Physics; Mathematics","score_opus":0.04488835756362002,"score_gpt":0.319430853069567,"score_spread":0.27454249550594695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307345599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2681114,0.00087195856,0.6940457,0.0033608696,0.0003113852,0.0010141124,0.000018333298,0.0013053975,0.030960826],"genre_scores_gemma":[0.6679881,0.000023573615,0.3318995,0.000011464345,0.0000042092183,0.000050258186,4.976691e-7,0.0000028033542,0.000019596693],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982517,0.00007453095,0.00023649119,0.0004343107,0.0007451014,0.00025789547],"domain_scores_gemma":[0.9991478,0.00022350751,0.00013838413,0.00033487994,0.00013331298,0.000022132872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018308613,0.00008393739,0.00015076919,0.00056894566,0.0005326346,0.00006618773,0.00094224536,0.000041718384,0.000009146844],"category_scores_gemma":[0.00014089342,0.00007784019,0.000017242248,0.0016775142,0.0020782882,0.0005119113,0.0011376688,0.00028072423,8.0150596e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050114093,0.00014219068,0.0008388917,0.000101331265,0.000008491644,0.000008260876,0.0012855325,0.00009881868,0.46262696,0.246547,0.0005641099,0.2877283],"study_design_scores_gemma":[0.00021763144,0.0006272436,0.001365037,0.000056917695,0.0000027969368,0.00006239048,0.0036977897,0.02408349,0.58996904,0.379516,0.00019991687,0.00020170069],"about_ca_topic_score_codex":0.0000121849,"about_ca_topic_score_gemma":0.00000254085,"teacher_disagreement_score":0.3998767,"about_ca_system_score_codex":0.000053483836,"about_ca_system_score_gemma":0.00013558673,"threshold_uncertainty_score":0.7657539},"labels":[],"label_agreement":null},{"id":"W4307898148","doi":"10.1016/j.neucom.2022.10.070","title":"Efficient subsampling of realistic images from GANs conditional on a class or a continuous variable","year":2022,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Benchmark (surveying); Pattern recognition (psychology); Artificial intelligence; Consistency (knowledge bases); Sampling (signal processing); Image (mathematics); Feature (linguistics); Class (philosophy); Conditional probability distribution; Variable (mathematics); Algorithm; Mathematics; Statistics; Computer vision","score_opus":0.019343084341807287,"score_gpt":0.2711750593142671,"score_spread":0.2518319749724598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307898148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027113795,0.00003194502,0.9709884,0.00019496237,0.00020016386,0.00020066054,0.00006420785,0.00055420306,0.0006516485],"genre_scores_gemma":[0.611332,3.810561e-7,0.38801995,0.00052281993,0.000047045585,0.000024378885,0.00001231876,0.000016504397,0.000024586758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980828,0.0001446679,0.00038350464,0.00058156904,0.0004885491,0.0003188906],"domain_scores_gemma":[0.9980464,0.0009956857,0.00033804,0.00044223593,0.00012103901,0.000056613302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031843546,0.0001705478,0.00026215386,0.00014725253,0.00043486248,0.00009528381,0.00092124916,0.000025129631,0.0000336078],"category_scores_gemma":[0.00024293025,0.0001697185,0.000052045034,0.00057535956,0.00006207859,0.0000819637,0.0008047961,0.0003626698,0.0000028123343],"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":[0.00018317692,0.00085074117,0.0004008782,0.0001119656,0.000040494677,0.00037517972,0.0012983311,0.7580825,0.15624923,0.060852773,0.0018961352,0.019658571],"study_design_scores_gemma":[0.00041328493,0.0002884716,0.00043097368,0.000076926066,0.000009222451,0.00006515226,0.00004460376,0.97654605,0.006686276,0.014369004,0.00084123056,0.0002287908],"about_ca_topic_score_codex":0.00004456387,"about_ca_topic_score_gemma":3.2736642e-7,"teacher_disagreement_score":0.5842182,"about_ca_system_score_codex":0.00008216722,"about_ca_system_score_gemma":0.00013598744,"threshold_uncertainty_score":0.692092},"labels":[],"label_agreement":null},{"id":"W4309235608","doi":"10.1080/2150704x.2022.2136019","title":"Super-resolution of Sentinel-2 images using Wasserstein GAN","year":2022,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Mean squared error; Image resolution; Resolution (logic); Computer science; Superresolution; Remote sensing; Satellite; Artificial intelligence; High resolution; Spectral bands; Pattern recognition (psychology); Generative adversarial network; Image (mathematics); Mathematics; Geology; Statistics; Physics","score_opus":0.01876400338812535,"score_gpt":0.2602951166165042,"score_spread":0.24153111322837884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309235608","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092932515,0.00006912661,0.9031227,0.003097339,0.00016818637,0.0000844306,0.0000012069311,0.0003602143,0.00016425192],"genre_scores_gemma":[0.3341782,0.0000020169625,0.66473943,0.0009974124,0.000033032306,5.743426e-8,0.0000019782335,0.000018285418,0.000029572378],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983911,0.00017352306,0.00027948437,0.00041374314,0.00041218792,0.000329954],"domain_scores_gemma":[0.99901813,0.00006281508,0.00020770462,0.00059122907,0.00007709051,0.00004300924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038681968,0.00015414934,0.00019934811,0.00023790932,0.0003405842,0.00006864172,0.0004841722,0.000024597335,0.00000361498],"category_scores_gemma":[0.00007237907,0.0001808884,0.000084492305,0.00060863927,0.00009957493,0.0004338805,0.00043156988,0.00024593805,0.0000022457432],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045926804,0.000008677619,0.000015053591,0.000020973883,0.000007411502,0.000053715325,0.00023525319,0.0017371648,0.97944534,0.000034566987,0.00075339014,0.017683888],"study_design_scores_gemma":[0.00014636383,0.00001324296,0.000043328608,0.00005068081,0.000010649729,0.00022671152,0.000043994612,0.8367314,0.16080967,0.0009610217,0.0007468504,0.00021610183],"about_ca_topic_score_codex":0.00014163187,"about_ca_topic_score_gemma":5.9889163e-7,"teacher_disagreement_score":0.8349942,"about_ca_system_score_codex":0.00019919548,"about_ca_system_score_gemma":0.000052617306,"threshold_uncertainty_score":0.73764145},"labels":[],"label_agreement":null},{"id":"W4311257085","doi":"10.1016/j.bspc.2022.104450","title":"Multi-level GAN based enhanced CT scans for liver cancer diagnosis","year":2022,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Advanced Image Processing 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 Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Preprocessor; Artificial intelligence; Pattern recognition (psychology); Metric (unit); Generative adversarial network; Similarity (geometry); Computer-aided diagnosis; Noise (video); Image (mathematics); Computer vision","score_opus":0.0309242260044258,"score_gpt":0.29546869910911955,"score_spread":0.26454447310469376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311257085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030610847,0.0036345897,0.9927209,0.0023304392,0.000090467496,0.00037974777,0.000120430086,0.00040321838,0.000014109422],"genre_scores_gemma":[0.7965512,0.00002247153,0.1979898,0.0022784297,0.00008581125,0.0029738164,0.000007797142,0.000019676158,0.00007095761],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805945,0.000081745704,0.00029059747,0.00062655384,0.0004819025,0.00045976008],"domain_scores_gemma":[0.99902755,0.00024226768,0.00018455856,0.0001772575,0.00015327384,0.00021511143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004356457,0.0002168672,0.00028551504,0.00014288966,0.00082804787,0.00019279693,0.00069948245,0.000042279604,0.000067833585],"category_scores_gemma":[0.0000907935,0.00019476727,0.00006617122,0.00047466837,0.00022215414,0.00035653557,0.00011139515,0.0002390362,8.0873696e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059012804,0.00026413638,0.0001054769,0.00013877674,0.000015822741,0.000018193636,0.00015954807,0.000041136518,0.02649034,0.000038055652,0.000775012,0.9718945],"study_design_scores_gemma":[0.0028046696,0.00031618352,0.00017685408,0.00012620738,0.000035342222,0.0000121378,0.000029732317,0.96932954,0.012133363,0.00084844924,0.013812936,0.0003746021],"about_ca_topic_score_codex":0.000043946668,"about_ca_topic_score_gemma":0.0000042333636,"teacher_disagreement_score":0.9715199,"about_ca_system_score_codex":0.000109526605,"about_ca_system_score_gemma":0.0004559408,"threshold_uncertainty_score":0.79423785},"labels":[],"label_agreement":null},{"id":"W4311969913","doi":"10.3390/s22249628","title":"Image Translation by Ad CycleGAN for COVID-19 X-Ray Images: A New Approach for Controllable GAN","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Institutes of Health","keywords":"Mean squared error; Translation (biology); Image translation; Computer science; Artificial intelligence; Image (mathematics); Image quality; Pattern recognition (psychology); Algorithm; Mathematics; Statistics","score_opus":0.030704156902123907,"score_gpt":0.3086819293091927,"score_spread":0.2779777724070688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311969913","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000693544,0.00042984073,0.99142796,0.004870379,0.00008117255,0.001536585,0.0001377442,0.00083845516,0.00060851994],"genre_scores_gemma":[0.010876554,0.000007618758,0.98432964,0.001555244,0.000045982124,0.00058552355,0.000087203385,0.000042773034,0.0024694353],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824935,0.000088355955,0.00028727102,0.00067359407,0.00026894725,0.00043250888],"domain_scores_gemma":[0.9987378,0.00030718755,0.00017460318,0.0004935943,0.00009251225,0.00019427335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057467376,0.00020240489,0.0002605984,0.00012417523,0.00058053934,0.00020251366,0.00079443894,0.000046413803,0.000015710384],"category_scores_gemma":[0.0002937731,0.00021814054,0.00012870404,0.00037673913,0.00006158034,0.00058320205,0.00007564884,0.00015034473,0.0000022396962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007370683,0.0004662032,0.000013610041,0.0005746665,0.000090621215,0.0000132377445,0.005981211,0.015846036,0.519014,0.0021103423,0.33984482,0.11530819],"study_design_scores_gemma":[0.0022231566,0.00019952781,9.703681e-7,0.0000037909183,0.000020690815,0.000014052378,0.00020900827,0.75393164,0.011147266,0.016370444,0.21553144,0.00034798085],"about_ca_topic_score_codex":0.000031803997,"about_ca_topic_score_gemma":0.0000014853838,"teacher_disagreement_score":0.7380856,"about_ca_system_score_codex":0.00017041928,"about_ca_system_score_gemma":0.00027213173,"threshold_uncertainty_score":0.88955134},"labels":[],"label_agreement":null},{"id":"W4312102546","doi":"10.1109/tencon55691.2022.9977590","title":"Data Augmentation Methods for Low Resolution Facial Images","year":2022,"lang":"en","type":"article","venue":"TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Overfitting; Computer science; Artificial intelligence; Regularization (linguistics); Pattern recognition (psychology); Face (sociological concept); Set (abstract data type); Data set; Training set; Resolution (logic); Image resolution; Machine learning; Data mining; Artificial neural network","score_opus":0.11438446735760073,"score_gpt":0.4027301345308763,"score_spread":0.2883456671732756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312102546","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020404521,0.00057938753,0.99135405,0.0029707348,0.0014552736,0.0011787503,0.00024330622,0.0011190729,0.0008953746],"genre_scores_gemma":[0.024952183,0.0003359017,0.96501243,0.0010447829,0.0002683184,0.0017224543,0.0006683733,0.000082171835,0.0059133708],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99484795,0.0008458544,0.00081762223,0.0018740455,0.00080782856,0.00080669054],"domain_scores_gemma":[0.9956418,0.00046180096,0.00078122324,0.0024327638,0.00048647635,0.00019590455],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0023484123,0.00048593586,0.00056171464,0.00048935314,0.0013052158,0.00042435367,0.0047078184,0.00013153089,0.00047327037],"category_scores_gemma":[0.0005499344,0.00053260813,0.000164089,0.0011838607,0.00029225938,0.0032867491,0.0022505547,0.0006992021,0.000032213487],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026232,0.00032938362,0.000050570998,0.0001827522,0.0000771457,0.0000671436,0.0011561326,0.000276108,0.11155648,0.009692062,0.11371506,0.7626348],"study_design_scores_gemma":[0.001579924,0.0008162648,0.00008568735,0.000092088434,0.000080208425,0.00020287653,0.0010649749,0.7537446,0.025791053,0.072055794,0.14315157,0.0013349466],"about_ca_topic_score_codex":0.00006643703,"about_ca_topic_score_gemma":0.00001577462,"teacher_disagreement_score":0.7612999,"about_ca_system_score_codex":0.000480857,"about_ca_system_score_gemma":0.00079670025,"threshold_uncertainty_score":0.99999493},"labels":[],"label_agreement":null},{"id":"W4312287303","doi":"10.1109/tai.2022.3224417","title":"Ultralight-Weight Three-Prior Convolutional Neural Network for Single Image Super Resolution","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Convolutional neural network; Benchmark (surveying); Computer science; Task (project management); Image (mathematics); Artificial intelligence; Convolution (computer science); Deep learning; Resolution (logic); Artificial neural network; Focus (optics); Pattern recognition (psychology); Image resolution; Superresolution; Optimization problem; Machine learning; Algorithm; Engineering; Geography","score_opus":0.054053651219946056,"score_gpt":0.2977028328842106,"score_spread":0.24364918166426458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312287303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006508346,0.00009737838,0.9941726,0.0018366125,0.0017236558,0.0005657006,0.00006467959,0.00075704517,0.00013151794],"genre_scores_gemma":[0.57606035,0.000006833465,0.42276198,0.00041365772,0.00015070756,0.0004514563,0.000006940432,0.00002880921,0.00011926996],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975364,0.000114019924,0.00052973046,0.00074034045,0.00047885452,0.0006006197],"domain_scores_gemma":[0.99862033,0.00031472693,0.00014152762,0.0005807878,0.00023166137,0.00011097999],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.000431274,0.00025908003,0.00021857594,0.00019078207,0.0017279733,0.00019606075,0.0010153842,0.00006897824,0.00017186743],"category_scores_gemma":[0.000025499738,0.00028613102,0.00019986159,0.00085322146,0.00021226601,0.00090093876,0.000017355553,0.00046491568,0.000043482152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000554716,0.0014935321,0.0000061197934,0.000042432028,0.000057580786,0.000024381527,0.0007486532,0.3093661,0.2534897,0.07094498,0.0023860538,0.36088574],"study_design_scores_gemma":[0.000050778955,0.00042561843,0.000004272972,0.000015513453,0.000015737838,0.000038385257,0.00003693866,0.6037853,0.29712012,0.09642067,0.0017523882,0.00033430045],"about_ca_topic_score_codex":0.000032348395,"about_ca_topic_score_gemma":0.000065003056,"teacher_disagreement_score":0.57540953,"about_ca_system_score_codex":0.00030786157,"about_ca_system_score_gemma":0.000113868446,"threshold_uncertainty_score":0.9999591},"labels":[],"label_agreement":null},{"id":"W4312298769","doi":"10.33965/ict_wbc_eh2022_202204l022","title":"SINGLE MR IMAGE SUPER-RESOLUTION USING GENERATIVE ADVERSARIAL NETWORK","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Generative adversarial network; Adversarial system; Resolution (logic); Image resolution; Computer science; Generative grammar; Superresolution; Artificial intelligence; Computer vision; Image (mathematics); Pattern recognition (psychology)","score_opus":0.029356140963108426,"score_gpt":0.27576201451554977,"score_spread":0.24640587355244134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312298769","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011704589,0.00013756122,0.9947332,0.00044159344,0.00047703972,0.00014355252,0.0000020782156,0.0007679003,0.0021265894],"genre_scores_gemma":[0.058902893,0.0000013446313,0.9399625,0.0006054081,0.00022465932,0.00002452819,0.0000042338133,0.0000137772395,0.0002606217],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860793,0.00013674417,0.00018669288,0.00041134845,0.00030689844,0.0003503634],"domain_scores_gemma":[0.9993619,0.000029923034,0.00008572035,0.00038350394,0.000088666755,0.000050340353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028259272,0.0001320792,0.0001273019,0.00006995557,0.00076585315,0.00016893467,0.00069883576,0.000025751187,0.00009304996],"category_scores_gemma":[0.000036015357,0.0001391648,0.00004896123,0.00056958623,0.00006195779,0.001182144,0.0011486633,0.00019415644,0.0000055239266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003474161,0.0002819821,0.0000728272,0.000011228618,0.000026297737,0.000080820544,0.0012420638,0.040223617,0.8621409,0.049279172,0.024180287,0.022426097],"study_design_scores_gemma":[0.00020428415,0.00013019564,0.000007555091,0.0000052780683,0.000005007069,0.000044033684,0.000042119413,0.9441231,0.02398321,0.023829784,0.0073819296,0.00024351115],"about_ca_topic_score_codex":0.000038668317,"about_ca_topic_score_gemma":0.0000038795843,"teacher_disagreement_score":0.9038995,"about_ca_system_score_codex":0.0003020385,"about_ca_system_score_gemma":0.000106148545,"threshold_uncertainty_score":0.58904004},"labels":[],"label_agreement":null},{"id":"W4312344003","doi":"10.1109/iscas48785.2022.9937612","title":"Does Video Compression Impact Tracking Accuracy?","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Data compression; Compression (physics); Tracking (education); Computer vision; Artificial intelligence","score_opus":0.026790228638664423,"score_gpt":0.3121599568375748,"score_spread":0.2853697281989104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312344003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16562621,0.001354622,0.7949137,0.00534145,0.018618526,0.001548665,0.00034438778,0.0017971054,0.010455318],"genre_scores_gemma":[0.9976567,0.00006520041,0.00059920206,0.00030715734,0.00030817665,0.00019671643,0.000017148319,0.000029340536,0.0008203469],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972607,0.00021291428,0.000482237,0.0007144424,0.0010112217,0.0003184937],"domain_scores_gemma":[0.998515,0.00030258298,0.00039688006,0.00049584726,0.00017021586,0.00011947632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006285851,0.00026388754,0.00028592173,0.0002265269,0.0005930836,0.00071473065,0.0014375779,0.000054087657,0.0000803461],"category_scores_gemma":[0.00006827959,0.00018486418,0.00011392828,0.0002712145,0.000041841075,0.0010945344,0.00048284905,0.00041136562,0.000010891549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009732031,0.00078087265,0.0075827762,0.00021779469,0.00032200964,0.00041746363,0.0049199155,0.017074803,0.80171543,0.03534349,0.018158447,0.11336965],"study_design_scores_gemma":[0.0018711697,0.0009328775,0.0036335622,0.0007037369,0.000033367516,0.0014226019,0.00064252084,0.8611391,0.024965478,0.006430212,0.096460864,0.0017644777],"about_ca_topic_score_codex":0.00008806946,"about_ca_topic_score_gemma":0.0000022703596,"teacher_disagreement_score":0.84406435,"about_ca_system_score_codex":0.00037336114,"about_ca_system_score_gemma":0.0000547566,"threshold_uncertainty_score":0.7538543},"labels":[],"label_agreement":null},{"id":"W4312458399","doi":"10.1007/978-3-031-20077-9_28","title":"Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Upsampling; Artificial intelligence; Object detection; Degradation (telecommunications); Transformation (genetics); Code (set theory); Computer vision; Pattern recognition (psychology); Representation (politics); Object (grammar); Image (mathematics)","score_opus":0.062232106857731215,"score_gpt":0.2976619037486506,"score_spread":0.23542979689091936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312458399","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004217643,0.00038195789,0.99669605,0.00023821852,0.0006237395,0.0006792338,0.0000046435616,0.0007635143,0.00019090257],"genre_scores_gemma":[0.06373719,0.0001476506,0.9353469,0.00034116313,0.00020127953,0.00015304399,0.000007858472,0.00003536942,0.00002956398],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965617,0.00008013402,0.00051482976,0.0015350156,0.0008184139,0.0004898747],"domain_scores_gemma":[0.99788725,0.0006028485,0.00035887607,0.0007568174,0.00029012284,0.00010406333],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015512429,0.00041977476,0.00039656012,0.0007252351,0.0008338924,0.0005275214,0.0014175511,0.00015478728,0.0000054864586],"category_scores_gemma":[0.00021629706,0.00044140007,0.000093444556,0.00055401505,0.00030816297,0.0027409093,0.0010817477,0.0006103902,0.0000019007992],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022533279,0.00002301521,0.0000073060105,0.00012057302,0.0000065891686,0.00000689007,0.0008556452,0.0023465261,0.0039125695,0.0054340092,0.000001434214,0.9872629],"study_design_scores_gemma":[0.00023705084,0.00035470893,0.000040390736,0.00017701213,0.000008312728,0.000044741904,6.800298e-7,0.7531342,0.022376595,0.22259784,0.00048592122,0.00054255186],"about_ca_topic_score_codex":0.000043916854,"about_ca_topic_score_gemma":0.00003797092,"teacher_disagreement_score":0.9867204,"about_ca_system_score_codex":0.0006338975,"about_ca_system_score_gemma":0.00036328757,"threshold_uncertainty_score":0.9998038},"labels":[],"label_agreement":null},{"id":"W4312509687","doi":"10.1109/iscas48785.2022.9937894","title":"DSegAN: A Deep Light-weight Segmentation-based Attention Network for Image Restoration","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Computer science; Feature (linguistics); Image restoration; Benchmark (surveying); Discriminative model; Image segmentation; Computer vision; Pattern recognition (psychology); Segmentation; Image (mathematics); Feature detection (computer vision); Pixel; Feature extraction; Image texture; Image processing; Geography; Cartography","score_opus":0.01747603524583953,"score_gpt":0.27467119912382143,"score_spread":0.2571951638779819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312509687","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033357479,0.0002693641,0.987247,0.0028118165,0.0038578943,0.0008578974,0.000056976238,0.00031749436,0.0012457933],"genre_scores_gemma":[0.96726567,0.000028244945,0.026806787,0.0009128148,0.0009999175,0.0022443398,0.00022690717,0.000058227182,0.0014571046],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976284,0.00018064646,0.00049907557,0.0006472344,0.00076422706,0.000280441],"domain_scores_gemma":[0.99868023,0.0001754002,0.0004223335,0.00035093856,0.00029297968,0.0000781091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073692243,0.00021870453,0.00021621164,0.00018949066,0.0007046505,0.0005310281,0.00071560824,0.00005393621,0.000022667233],"category_scores_gemma":[0.000032638585,0.00022721864,0.0000959157,0.00032360858,0.000027849126,0.0007823275,0.000116506024,0.00019237785,0.0000081884455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020066198,0.0008085763,0.0023828128,0.0004221484,0.00028044585,0.00012490917,0.00206821,0.05154615,0.7425933,0.12190673,0.05366791,0.023998149],"study_design_scores_gemma":[0.0011716336,0.00049716607,0.0002113986,0.000117288364,0.000024090608,0.000103262886,0.00012794745,0.9616528,0.005383711,0.0028173444,0.027378703,0.0005146286],"about_ca_topic_score_codex":0.00002005899,"about_ca_topic_score_gemma":0.000007031041,"teacher_disagreement_score":0.9639299,"about_ca_system_score_codex":0.00042332246,"about_ca_system_score_gemma":0.000063021354,"threshold_uncertainty_score":0.9265707},"labels":[],"label_agreement":null},{"id":"W4312525628","doi":"10.33965/mccsis2022_202206l013","title":"MULTI-MODALITY IMAGE SUPER-RESOLUTION USING GENERATIVE ADVERSARIAL NETWORKS","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Image translation; Generative grammar; Image (mathematics); Computer science; Artificial intelligence; Translation (biology); Modality (human–computer interaction); Generative adversarial network; Superresolution; Deep learning; Computer vision; Resolution (logic)","score_opus":0.03513344909082635,"score_gpt":0.30834283757101566,"score_spread":0.27320938848018933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312525628","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006357979,0.0000940325,0.99720573,0.0004945977,0.00037375142,0.00018458263,0.0000035696717,0.00075907423,0.00024886735],"genre_scores_gemma":[0.1485232,0.0000021504754,0.85080695,0.00040922445,0.00008477865,0.000032440345,0.0000051371967,0.000010831354,0.00012527056],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998527,0.00019656737,0.00020875192,0.00047607286,0.00027630903,0.00031531352],"domain_scores_gemma":[0.99925476,0.000034205314,0.000090817004,0.0004534579,0.00010826189,0.000058481608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040333273,0.00014305853,0.0001381779,0.00007688118,0.00072064804,0.00014602375,0.0007765636,0.000034531156,0.000056425226],"category_scores_gemma":[0.000044385495,0.00014796732,0.00005719741,0.00044240322,0.00007662235,0.0012327271,0.0012927653,0.00028068834,0.0000024627293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105456966,0.0008581656,0.00047292805,0.000026848378,0.00006499003,0.000145267,0.00237915,0.3091039,0.61459804,0.035568457,0.006965438,0.029711343],"study_design_scores_gemma":[0.00027430546,0.00004020885,0.000052582567,0.0000024237058,0.0000036844442,0.000021024542,0.00003477291,0.9913685,0.004397885,0.0030420355,0.0005714092,0.00019117913],"about_ca_topic_score_codex":0.00012860901,"about_ca_topic_score_gemma":0.000009239556,"teacher_disagreement_score":0.68226457,"about_ca_system_score_codex":0.0002939137,"about_ca_system_score_gemma":0.000104291845,"threshold_uncertainty_score":0.60339326},"labels":[],"label_agreement":null},{"id":"W4312965115","doi":"10.1109/mmsp55362.2022.9949474","title":"A Gated Deep Model for Single Image Super-Resolution Reconstruction","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Image (mathematics); Computer vision; Iterative reconstruction; Deep learning; Content (measure theory); Pattern recognition (psychology); Image resolution; Superresolution; Mathematics","score_opus":0.03146529677265689,"score_gpt":0.2946295726004746,"score_spread":0.26316427582781776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312965115","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008626403,0.00024191847,0.9932851,0.0017800472,0.0013068464,0.0005760063,0.00006345205,0.0009701794,0.00091377465],"genre_scores_gemma":[0.2878919,0.000010920926,0.7093289,0.00072946126,0.00030061486,0.0005807623,0.0001179611,0.00007013755,0.0009693923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996275,0.00012536094,0.00069506315,0.0011294287,0.0011601665,0.00061495876],"domain_scores_gemma":[0.9979309,0.0003290296,0.000520105,0.00041366575,0.00064237753,0.00016392805],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007040263,0.00041568137,0.00032674937,0.0006068959,0.0010618393,0.0005213377,0.001800676,0.00012732402,0.00015740111],"category_scores_gemma":[0.0002979639,0.00046374078,0.00018444337,0.00083037553,0.00018257678,0.0019991356,0.00041670914,0.0007903995,0.000017693714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040739917,0.00062109536,0.00001869624,0.00005780474,0.000052841006,0.000025940522,0.0013910496,0.0874663,0.1722326,0.00042731623,0.0031584958,0.73414046],"study_design_scores_gemma":[0.00088087993,0.00015146458,0.000003225517,0.00013153297,0.000017220764,0.00011729991,0.00015388231,0.971634,0.015263845,0.009797644,0.0013483318,0.00050072145],"about_ca_topic_score_codex":0.0000058719033,"about_ca_topic_score_gemma":0.000004466708,"teacher_disagreement_score":0.8841677,"about_ca_system_score_codex":0.00094121444,"about_ca_system_score_gemma":0.0002732483,"threshold_uncertainty_score":0.9997814},"labels":[],"label_agreement":null},{"id":"W4317377546","doi":"10.1016/j.bspc.2023.104590","title":"Improving anisotropy resolution of computed tomography and annotation using 3D super-resolution network","year":2023,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Science and Technology Planning Project of Guangdong Province; Key Research and Development Program of Hunan Province of China; China Postdoctoral Science Foundation; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Iterative reconstruction; Context (archaeology); Computer vision; Segmentation; Image resolution; Image quality; Feature (linguistics); Image (mathematics)","score_opus":0.012749161705944163,"score_gpt":0.25428015693333267,"score_spread":0.2415309952273885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317377546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010039555,0.0024647224,0.9864107,0.000315084,0.00006071777,0.0001623353,0.0000041683884,0.00053589774,0.000006842795],"genre_scores_gemma":[0.69343585,0.000016071794,0.3063207,0.00009055563,0.00010863484,0.000009006128,0.00000704114,0.000009571146,0.0000025907248],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998333,0.00009602795,0.00037075309,0.00043784216,0.00036950962,0.00039291554],"domain_scores_gemma":[0.9992006,0.00011323391,0.00023282536,0.00013035408,0.000185666,0.0001373521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060876255,0.00016935672,0.00026150479,0.00025622835,0.00039727258,0.00016660686,0.00022407963,0.00012713952,8.9650143e-7],"category_scores_gemma":[0.00006316369,0.00015515866,0.00003515177,0.001255653,0.00036097824,0.0006649362,0.00015023614,0.00015492216,4.733528e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008028927,0.000058753038,0.00069105753,0.0003183122,0.000020539754,0.00001583696,0.00027055942,0.0003772878,0.18238835,0.00067550165,0.0001131605,0.81499034],"study_design_scores_gemma":[0.0007100866,0.00018208381,0.0011641841,0.00024561817,0.000022902415,0.000023441899,0.000020153373,0.99154836,0.00045676364,0.005308429,0.00014712503,0.00017084712],"about_ca_topic_score_codex":0.00003701375,"about_ca_topic_score_gemma":7.613887e-7,"teacher_disagreement_score":0.99117106,"about_ca_system_score_codex":0.000028249036,"about_ca_system_score_gemma":0.00010442607,"threshold_uncertainty_score":0.6327187},"labels":[],"label_agreement":null},{"id":"W4317394338","doi":"10.3390/app13031245","title":"Dual-Path Adversarial Generation Network for Super-Resolution Reconstruction of Remote Sensing Images","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Department of Science and Technology of Jilin Province; Education Department of Jilin Province","keywords":"Computer science; Discriminator; Artificial intelligence; Feature (linguistics); Computer vision; Remote sensing; Pattern recognition (psychology); Geography; Telecommunications","score_opus":0.03597837892627352,"score_gpt":0.28840280016439174,"score_spread":0.2524244212381182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317394338","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008030867,0.000049300914,0.9896488,0.0003399047,0.0005857911,0.00026425163,0.0000024325718,0.0004919844,0.0005866671],"genre_scores_gemma":[0.18515673,0.000016174514,0.8145144,0.000045286422,0.00023020066,0.000003837861,0.000005046484,0.000005478331,0.000022829423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986718,0.0000273429,0.00025682538,0.00045281457,0.00027899773,0.00031216632],"domain_scores_gemma":[0.9993438,0.00010596217,0.00017826051,0.00022751465,0.00011739878,0.000027062217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010985661,0.00010673327,0.00014253812,0.00014896042,0.0005966862,0.00013086849,0.00032656844,0.00005473458,8.58743e-7],"category_scores_gemma":[0.000098309974,0.000101651465,0.00003900204,0.0012261054,0.000286918,0.0006809241,0.00013432889,0.000060952563,0.0000042875813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000073666615,0.000004160949,0.000008349775,0.000016528298,0.0000037115205,8.2773494e-7,0.00025581522,0.0034090572,0.33785382,0.012573794,0.0022098175,0.6436568],"study_design_scores_gemma":[0.00013210328,0.000051214287,0.00003354298,0.000025480047,0.000004299588,0.000011140652,0.000052654337,0.8268208,0.09121953,0.0811471,0.00038558367,0.00011659291],"about_ca_topic_score_codex":0.000017887052,"about_ca_topic_score_gemma":0.0000051088414,"teacher_disagreement_score":0.8234117,"about_ca_system_score_codex":0.000032906366,"about_ca_system_score_gemma":0.00010666806,"threshold_uncertainty_score":0.45892882},"labels":[],"label_agreement":null},{"id":"W4319998685","doi":"10.18280/ria.360616","title":"Study of Deep Learning-based models for Single Image Super-Resolution","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Deep learning; Computer science; Image (mathematics); Pattern recognition (psychology); Computer vision","score_opus":0.05735399554474138,"score_gpt":0.302804144316914,"score_spread":0.2454501487721726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319998685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005662269,0.00020006455,0.9923716,0.00018908804,0.000120180994,0.00066818483,0.0000032106811,0.00035457272,0.00043082598],"genre_scores_gemma":[0.77227926,0.0000019418758,0.22723413,0.000038610768,0.000013671382,0.00027456434,0.000004517654,0.000018714574,0.00013457613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982395,0.00013988702,0.00046664037,0.0005384706,0.00029268998,0.00032280633],"domain_scores_gemma":[0.99858963,0.000220791,0.00022785272,0.0006486116,0.00026248826,0.00005061711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060808676,0.00015618565,0.00022506992,0.00019538726,0.0004996247,0.00007369071,0.0010843277,0.00002934729,0.000028917295],"category_scores_gemma":[0.00017216464,0.00017860482,0.00009198426,0.00070793653,0.00007210431,0.00053671433,0.0004158707,0.00023180166,0.000005955915],"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":[0.000031723044,0.0012415528,0.000024938801,0.00004680479,0.0000064873952,0.000008431884,0.003546112,0.9273,0.03821993,0.0024138317,0.000050488918,0.027109709],"study_design_scores_gemma":[0.0000756079,0.0013557747,0.0000010978947,0.000012980467,0.0000073662977,0.00000929995,0.0018139265,0.88376844,0.101362206,0.0109985825,0.00042469316,0.00017005144],"about_ca_topic_score_codex":0.000018621635,"about_ca_topic_score_gemma":0.0000053976114,"teacher_disagreement_score":0.766617,"about_ca_system_score_codex":0.0001226295,"about_ca_system_score_gemma":0.00004478015,"threshold_uncertainty_score":0.72832936},"labels":[],"label_agreement":null},{"id":"W4321231835","doi":"10.1007/978-3-031-25066-8_8","title":"AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Processing 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":"Huawei Technologies (Canada)","funders":"Julius-Maximilians-Universität Würzburg; MediaTek; Eidgenössische Technische Hochschule Zürich","keywords":"Computer science; Track (disk drive); Artificial intelligence; Computer vision; Image (mathematics); Resolution (logic); Image resolution; Computer graphics (images)","score_opus":0.03778798908087231,"score_gpt":0.342393482447527,"score_spread":0.30460549336665466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321231835","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000069527905,0.0011090883,0.99591315,0.0011943856,0.0003905708,0.0003508749,0.00016778815,0.00029147195,0.0005757181],"genre_scores_gemma":[0.0019975083,0.00060449116,0.9967928,0.00034193735,0.000092284026,0.000011386987,0.0000454194,0.000036962047,0.00007723355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961893,0.00010716716,0.0006722081,0.0018365681,0.0007112852,0.00048346864],"domain_scores_gemma":[0.99632883,0.001347343,0.00042095073,0.0015472805,0.00021589731,0.0001397261],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020687357,0.00048254817,0.0006055107,0.00085368764,0.00023476567,0.00031027745,0.002059706,0.00024844395,0.0000018012564],"category_scores_gemma":[0.0005247876,0.00044913383,0.000046937377,0.0005230756,0.001379389,0.0009231713,0.0028077774,0.00076844153,0.0000037508216],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040830408,0.00004674972,0.0000017435975,0.00018861298,0.000011178787,0.00007164994,0.0006693914,0.00076852523,0.005338141,0.011544186,0.0002612818,0.9810577],"study_design_scores_gemma":[0.0004553945,0.0005455055,0.000048692054,0.0008619786,0.000010606043,0.000045039433,3.491523e-7,0.72041136,0.010033574,0.2639402,0.003025191,0.0006221147],"about_ca_topic_score_codex":0.000026250285,"about_ca_topic_score_gemma":0.000021714732,"teacher_disagreement_score":0.9804356,"about_ca_system_score_codex":0.00009634876,"about_ca_system_score_gemma":0.00018248717,"threshold_uncertainty_score":0.99979603},"labels":[],"label_agreement":null},{"id":"W4322746788","doi":"10.1007/978-3-031-26313-2_5","title":"Blind Image Super-Resolution with Degradation-Aware Adaptation","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Upsampling; Degradation (telecommunications); Artificial intelligence; Generalization; Representation (politics); Image (mathematics); Image restoration; Encoder; Computer vision; Adaptation (eye); Image resolution; Pattern recognition (psychology); Image processing; Mathematics","score_opus":0.02943291844688085,"score_gpt":0.27351533011510615,"score_spread":0.2440824116682253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322746788","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011689594,0.00011370799,0.99619126,0.00092170935,0.00050394307,0.00045789342,0.000006351786,0.0010854239,0.0007080424],"genre_scores_gemma":[0.008378276,0.00003745155,0.99021244,0.00037956622,0.00018611058,0.000029423009,0.000024377758,0.00006303927,0.0006893411],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99608743,0.000030463796,0.00045606052,0.0016290329,0.001184031,0.00061300583],"domain_scores_gemma":[0.9972991,0.00033210707,0.0003310965,0.0012597457,0.00064719655,0.00013078077],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006277718,0.00052666443,0.00039921727,0.001088814,0.00037448033,0.0008099596,0.0025895436,0.0002530989,0.0000062945205],"category_scores_gemma":[0.0001306006,0.00046610108,0.00007028368,0.0012534624,0.0007623799,0.0022233978,0.00087899156,0.0007464477,0.00006008732],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027449267,0.00003652077,0.000019925697,0.00009453681,0.000013652529,0.00018270547,0.0010488172,0.026385643,0.00079962704,0.015627556,0.000067799476,0.95569575],"study_design_scores_gemma":[0.00025619293,0.0001780654,0.000034907902,0.000511295,0.0000074781074,0.000059868617,4.4139367e-7,0.838465,0.0017680824,0.15790777,0.00022950575,0.00058141246],"about_ca_topic_score_codex":0.000027090473,"about_ca_topic_score_gemma":0.00015601264,"teacher_disagreement_score":0.95511436,"about_ca_system_score_codex":0.0003743507,"about_ca_system_score_gemma":0.0007823571,"threshold_uncertainty_score":0.99977905},"labels":[],"label_agreement":null},{"id":"W4323844569","doi":"10.18280/isi.280112","title":"FSRSI: New Deep Learning-Based Approach for Super-Resolution of Multispectral Satellite Images","year":2023,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multispectral image; Satellite; Remote sensing; Deep learning; Computer science; Artificial intelligence; Resolution (logic); Computer vision; Geology; Engineering; Aerospace engineering","score_opus":0.028435609569201603,"score_gpt":0.2734566359424432,"score_spread":0.24502102637324158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323844569","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006114816,0.0027927852,0.9921094,0.0004311084,0.0004737035,0.0009489991,0.000035295237,0.0012324993,0.0013646962],"genre_scores_gemma":[0.17695755,0.0002980729,0.8207575,0.00009481102,0.00014145959,0.00014954813,0.00039608052,0.000036409812,0.0011685615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972621,0.00013455884,0.0010248536,0.000347967,0.00045381137,0.0007766995],"domain_scores_gemma":[0.99763083,0.00024083785,0.0007188172,0.0004517849,0.00079627836,0.000161419],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009431059,0.0003651723,0.00042474322,0.0006288722,0.0004893663,0.0005465943,0.0006906283,0.00027043276,0.000009981407],"category_scores_gemma":[0.0010958896,0.00041422347,0.00020588547,0.0016296214,0.00043007254,0.008137966,0.00018560451,0.00031016607,0.00006002174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001076565,0.00007789019,0.00040152678,0.0036589853,0.000042883876,0.0000026788996,0.011887405,0.07728692,0.0024372353,0.004870259,0.0027809741,0.8964456],"study_design_scores_gemma":[0.00073605764,0.00035249474,0.0012489734,0.00042882017,0.000033447588,0.000022065467,0.00046205657,0.95192033,0.02072952,0.007100325,0.016543772,0.00042211628],"about_ca_topic_score_codex":0.00020982107,"about_ca_topic_score_gemma":0.000007193777,"teacher_disagreement_score":0.89602345,"about_ca_system_score_codex":0.00041645372,"about_ca_system_score_gemma":0.00038043238,"threshold_uncertainty_score":0.99983096},"labels":[],"label_agreement":null},{"id":"W4353100340","doi":"10.18280/ts.400135","title":"Deep Learning and Fuzzy Logic Based Intelligent Technique for the Image Enhancement and Edge Detection Framework","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fuzzy logic; Computer science; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Image (mathematics); Computer vision; Edge detection; Pattern recognition (psychology); Image processing","score_opus":0.020525257539429614,"score_gpt":0.2956666103508973,"score_spread":0.2751413528114677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353100340","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00034674132,0.00040288863,0.9969813,0.00079276937,0.000059024645,0.0008386065,7.2743103e-7,0.00053485675,0.00004306315],"genre_scores_gemma":[0.40496188,0.0001484521,0.59380376,0.00022999675,0.00004842473,0.0007683139,0.0000020996063,0.000014090532,0.00002299387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884176,0.000057276982,0.00022079956,0.00040044467,0.0001921655,0.00028757396],"domain_scores_gemma":[0.99913573,0.00044287837,0.00010815598,0.0001741109,0.00008505921,0.00005407879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007800253,0.00016766838,0.00012787533,0.00012406247,0.00042197853,0.00022030412,0.00030034815,0.000061496765,0.000011993425],"category_scores_gemma":[0.00011149699,0.00012951244,0.00003845767,0.00034421147,0.0001094396,0.00027458844,0.00019404352,0.00021810512,0.000003866994],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042639283,0.00005847378,0.00006739119,0.00014546775,0.000022734004,0.0000062500394,0.00067824486,0.00019582227,0.3592442,0.005220047,0.00007666923,0.63424206],"study_design_scores_gemma":[0.00021276089,0.000500794,0.00016523578,0.00009829356,0.000017935063,0.0000076217807,0.000109454515,0.51790375,0.4081982,0.069736496,0.0028015845,0.00024786795],"about_ca_topic_score_codex":0.0000021283756,"about_ca_topic_score_gemma":0.0000014054157,"teacher_disagreement_score":0.63399416,"about_ca_system_score_codex":0.0000488635,"about_ca_system_score_gemma":0.000018364299,"threshold_uncertainty_score":0.52813643},"labels":[],"label_agreement":null},{"id":"W4361303124","doi":"10.1007/s00371-023-02835-9","title":"HighBoostNet: a deep light-weight image super-resolution network using high-boost residual blocks","year":2023,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Advanced Image Processing 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":"Concordia University; University of Alberta","funders":"","keywords":"Residual; Computer science; Artificial intelligence; Block (permutation group theory); Distortion (music); Image (mathematics); Process (computing); Computer vision; Deep learning; Generalization; Set (abstract data type); Pattern recognition (psychology); Algorithm; Mathematics; Telecommunications","score_opus":0.017305888098877772,"score_gpt":0.29178709480230414,"score_spread":0.27448120670342635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361303124","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023911767,0.00024387978,0.9691898,0.0026214104,0.0009071087,0.00033073753,0.0000021612523,0.0026592726,0.00013384875],"genre_scores_gemma":[0.15728639,0.000029972634,0.83878934,0.0011498069,0.0023828347,0.00003443519,0.00001633973,0.000076043165,0.00023480749],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969659,0.00026461828,0.00045824662,0.00077409064,0.0005737367,0.0009634173],"domain_scores_gemma":[0.99823964,0.0002513243,0.00018645606,0.0009793175,0.00021276728,0.00013047649],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00076761603,0.00038501108,0.0003404636,0.0002324986,0.00082761544,0.0005534598,0.0018611846,0.00013627583,0.00001708662],"category_scores_gemma":[0.000030254,0.0002937928,0.000106074294,0.0016713866,0.00018625827,0.0010263579,0.0019893385,0.0004226625,0.00019783307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039738213,0.00093556446,0.0008404488,0.00032029964,0.0005408439,0.0014715529,0.009930296,0.051799443,0.21788844,0.08618177,0.37087348,0.25882047],"study_design_scores_gemma":[0.00031751575,0.00020314154,0.0006583871,0.000106297,0.000021691429,0.0000727425,0.0000069084117,0.96499175,0.0078034457,0.021010963,0.004337288,0.00046985474],"about_ca_topic_score_codex":0.00004174639,"about_ca_topic_score_gemma":0.000007208942,"teacher_disagreement_score":0.91319233,"about_ca_system_score_codex":0.000120773104,"about_ca_system_score_gemma":0.00008818412,"threshold_uncertainty_score":0.9999514},"labels":[],"label_agreement":null},{"id":"W4366999412","doi":"10.5194/gmd-16-2181-2023","title":"Improving trajectory calculations by FLEXPART 10.4+ using single-image super-resolution","year":2023,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Austrian Science Fund; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Deutsche Forschungsgemeinschaft","keywords":"Interpolation (computer graphics); Image resolution; Trajectory; Advection; Meteorology; Temporal resolution; Algorithm; Mathematics; Computer science; Physics; Image (mathematics); Artificial intelligence; Optics","score_opus":0.051567377736047026,"score_gpt":0.279311609743212,"score_spread":0.22774423200716498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366999412","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015045914,0.00006888762,0.9819583,0.00016494912,0.0004245151,0.00026736702,0.000016906492,0.0017679437,0.00028516294],"genre_scores_gemma":[0.040399447,0.000002012097,0.9514133,0.000070286915,0.000018684206,0.00007398358,0.00010040648,0.00003100874,0.007890924],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99704427,0.000046878686,0.00049153715,0.0009956608,0.0006715671,0.0007500621],"domain_scores_gemma":[0.99871737,0.000031402058,0.00015019214,0.0006841595,0.00026031907,0.00015656202],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00079885515,0.00026262234,0.0001914556,0.00044947574,0.00094956666,0.0005443009,0.0008794068,0.00008917693,0.000015895574],"category_scores_gemma":[0.00009977655,0.0002837107,0.00006159693,0.0014266244,0.00013762628,0.0014940284,0.00064139103,0.0001474323,0.00015221916],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035107741,0.00012608925,0.000011555971,0.000056361296,0.000009030818,0.00000939514,0.0014452287,0.011855356,0.9301646,0.0004039427,0.015155964,0.04075897],"study_design_scores_gemma":[0.00011913433,0.000008513695,0.000027015058,0.000038872236,0.000004190661,0.0000058188875,0.000019897765,0.94348615,0.048794534,0.0010364203,0.006110674,0.00034874788],"about_ca_topic_score_codex":0.000023905464,"about_ca_topic_score_gemma":0.0000090463345,"teacher_disagreement_score":0.93163085,"about_ca_system_score_codex":0.00046174676,"about_ca_system_score_gemma":0.00050873915,"threshold_uncertainty_score":0.9999615},"labels":[],"label_agreement":null},{"id":"W4372262736","doi":"10.1109/icassp49357.2023.10097260","title":"Image Completion Via Dual-Path Cooperative Filtering","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial intelligence; Path (computing); Convolution (computer science); Image (mathematics); Generalization; Dual (grammatical number); Feature (linguistics); Pattern recognition (psychology); Computer vision; Kernel (algebra); Image restoration; Image processing; Mathematics; Artificial neural network","score_opus":0.023446961136925695,"score_gpt":0.2990198386198073,"score_spread":0.2755728774828816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4372262736","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00067354954,0.000009933129,0.9910794,0.00075929106,0.00008367128,0.000099939716,0.000001235824,0.0032066582,0.0040863305],"genre_scores_gemma":[0.0679357,0.000008673169,0.9309678,0.00030936155,0.000027834207,0.000028771232,0.000006303557,0.000011116761,0.00070440443],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991353,0.00002917886,0.000138688,0.0003051972,0.00016444722,0.00022717407],"domain_scores_gemma":[0.99940914,0.000046541412,0.000041954285,0.00033320952,0.00012509365,0.000044089753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017284365,0.00010589089,0.00010835717,0.00010209434,0.00015233201,0.00018815552,0.00037820233,0.000024214349,0.000030451229],"category_scores_gemma":[0.00005115124,0.00009547727,0.000023422197,0.000640593,0.000048939826,0.0010700243,0.00044590517,0.00008853727,0.00036393764],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025278443,0.00003060795,0.00003873689,0.000029841573,0.000006712905,0.00009214383,0.0006829256,0.000035437286,0.9420267,0.015266628,0.01249386,0.029293884],"study_design_scores_gemma":[0.00012444888,0.00006299905,0.0005609145,0.000025526962,0.0000012132026,0.000029541103,0.000021325779,0.88902277,0.091026776,0.016700607,0.0022138464,0.00021000803],"about_ca_topic_score_codex":0.000008462658,"about_ca_topic_score_gemma":0.0000013983025,"teacher_disagreement_score":0.88898736,"about_ca_system_score_codex":0.000032116033,"about_ca_system_score_gemma":0.000020519392,"threshold_uncertainty_score":0.46778035},"labels":[],"label_agreement":null},{"id":"W4378085853","doi":"10.1111/cgf.14744","title":"Subpixel Deblurring of Anti‐Aliased Raster Clip‐Art","year":2023,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Northern Digital (Canada); University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Subpixel rendering; Computer science; Computer vision; Artificial intelligence; Pixel; Computer graphics (images); Raster graphics; Rendering (computer graphics); Inpainting; Deblurring; Image resolution; Image processing; Image restoration; Image (mathematics)","score_opus":0.019942284166347465,"score_gpt":0.2766780796836456,"score_spread":0.25673579551729814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378085853","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013225451,0.00008159811,0.98328424,0.00085284526,0.0005853974,0.0001690293,0.0000034764857,0.0016009301,0.0001970406],"genre_scores_gemma":[0.4585538,0.000067980116,0.53984094,0.0012221537,0.000091579306,0.000023993865,0.00001410383,0.000045526765,0.00013990846],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800265,0.00005334485,0.00043194558,0.0005537198,0.00037906962,0.00057927024],"domain_scores_gemma":[0.9984163,0.00012874525,0.00020092167,0.00094550883,0.00020360721,0.00010490729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003871024,0.0002371524,0.00032272044,0.0006101225,0.00016094734,0.00014678958,0.0015304545,0.000101032434,0.0000024134588],"category_scores_gemma":[0.00002791064,0.0002342881,0.00015891748,0.001871136,0.0001530514,0.0008523728,0.0011371176,0.0002319712,0.0000607792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039115694,0.00053473166,0.08162797,0.00080848474,0.0002303863,0.00024218018,0.0018636286,0.0005392421,0.04356966,0.5191587,0.17102697,0.18035889],"study_design_scores_gemma":[0.00045394147,0.00018488498,0.005010142,0.0002377127,0.000010225812,0.000033905726,0.000013269605,0.87903583,0.019909447,0.08058883,0.013966765,0.000555019],"about_ca_topic_score_codex":0.0000033171436,"about_ca_topic_score_gemma":0.0000022638174,"teacher_disagreement_score":0.8784966,"about_ca_system_score_codex":0.00001665785,"about_ca_system_score_gemma":0.0000500976,"threshold_uncertainty_score":0.95539916},"labels":[],"label_agreement":null},{"id":"W4379928090","doi":"10.1109/tpami.2023.3283979","title":"Blind Image Deconvolution Using Variational Deep Image Prior","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Prior probability; Artificial intelligence; Deconvolution; Computer science; Maximum a posteriori estimation; Image (mathematics); Blind deconvolution; Image restoration; Generalization; Pattern recognition (psychology); Benchmark (surveying); Computer vision; Pixel; Mathematics; Image processing; Algorithm; Maximum likelihood; Statistics; Bayesian probability","score_opus":0.027913970475709548,"score_gpt":0.3174776599293144,"score_spread":0.2895636894536049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379928090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014834604,0.000048220245,0.9973695,0.00032613252,0.00012368833,0.0001288184,0.000023071003,0.0004659508,0.00003111955],"genre_scores_gemma":[0.69596267,0.0001393464,0.30361247,0.00015655614,0.000018094775,0.000021792992,0.0000075609014,0.0000139205495,0.00006761005],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830854,0.0000742821,0.0003936532,0.00061383273,0.00031551547,0.000294155],"domain_scores_gemma":[0.9989683,0.0001392134,0.00015027358,0.00046338962,0.00017280744,0.000106020394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003482396,0.0002233566,0.00026090213,0.00094217074,0.00038901693,0.00028136212,0.0004751217,0.0000670026,0.00008719245],"category_scores_gemma":[0.000015926547,0.00021700385,0.00018281057,0.0023940294,0.00009840947,0.0009363048,0.000015361227,0.0002510118,0.00006401817],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020839056,0.00017616877,0.00021567542,0.000038559298,0.00036111407,0.000031230706,0.0005185938,0.021072002,0.024573635,0.00017736762,0.000010897255,0.9528039],"study_design_scores_gemma":[0.00008176773,0.000035414247,0.00053754944,0.000016268634,0.00017439554,0.000013847966,0.000017573582,0.9025198,0.09371341,0.0026579725,0.000011706104,0.00022029439],"about_ca_topic_score_codex":0.00024473283,"about_ca_topic_score_gemma":0.0001843384,"teacher_disagreement_score":0.9525836,"about_ca_system_score_codex":0.00007253186,"about_ca_system_score_gemma":0.00003955651,"threshold_uncertainty_score":0.88491607},"labels":[],"label_agreement":null},{"id":"W4380873810","doi":"10.1145/3591156.3591176","title":"Audio Super-resolution Using Feed-Forward Neural Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Encoder; Audio signal; Audio signal flow; Lossy compression; Audio signal processing; Speech recognition; Real-time computing; Computer hardware; Speech coding; Artificial intelligence","score_opus":0.03188736556588898,"score_gpt":0.2993861455030595,"score_spread":0.26749877993717047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380873810","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034876687,0.000081865284,0.9913021,0.00063336414,0.0002721224,0.00009312453,2.800077e-7,0.003480239,0.0006492592],"genre_scores_gemma":[0.35287926,0.0000115726825,0.6461411,0.00038279244,0.000094975185,0.000008962201,0.0000024295182,0.000014976551,0.00046394364],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881744,0.000032898166,0.00017403395,0.00035540023,0.00020294248,0.00041729482],"domain_scores_gemma":[0.9993306,0.000043190186,0.000049540293,0.000426026,0.000081140446,0.00006949323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022485432,0.00012858139,0.00012081402,0.00014433301,0.00021013588,0.00019391318,0.0006690556,0.00006028137,0.0000070356637],"category_scores_gemma":[0.00004850405,0.00011871775,0.000049902123,0.0010873738,0.000051469426,0.001182059,0.00052449125,0.00014673734,0.000031857144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031044383,0.00013477089,0.0048768255,0.000102756196,0.00005130615,0.0002582059,0.0009203643,0.3054093,0.05843967,0.05464791,0.053603023,0.5215248],"study_design_scores_gemma":[0.00006667224,0.000021266063,0.00031253792,0.000012411338,0.0000022643796,0.000020650838,0.000007590768,0.99228364,0.0012489659,0.0052646715,0.0006056366,0.00015370683],"about_ca_topic_score_codex":0.000024814073,"about_ca_topic_score_gemma":0.0000047184894,"teacher_disagreement_score":0.68687433,"about_ca_system_score_codex":0.00006367017,"about_ca_system_score_gemma":0.000028231361,"threshold_uncertainty_score":0.4841169},"labels":[],"label_agreement":null},{"id":"W4383535470","doi":"10.54254/2755-2721/4/20230430","title":"Fast CNN enhancement using channel attention and residual networks for image super-resolution","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Residual; Computer science; Artificial intelligence; Generalization; Similarity (geometry); Channel (broadcasting); Algorithm; Parametric statistics; Image (mathematics); Reset (finance); Deep learning; Pattern recognition (psychology); Activation function; Process (computing); Artificial neural network; Mathematics; Statistics","score_opus":0.013463833685373593,"score_gpt":0.24795767409886124,"score_spread":0.23449384041348764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383535470","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011629379,0.00009950878,0.9875341,0.000120348035,0.00006196557,0.00017690564,0.0000017998137,0.00036220645,0.000013806097],"genre_scores_gemma":[0.42734703,0.000011837716,0.5724594,0.00002788585,0.000059607595,0.000049003433,0.000027231368,0.000009876774,0.000008083924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930453,0.0000035504568,0.00013336334,0.00025709698,0.000110832036,0.00019062537],"domain_scores_gemma":[0.99973893,0.000069909154,0.00003538507,0.00006776718,0.0000490796,0.000038940452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001410236,0.00010490245,0.00009166881,0.000105912884,0.00014785526,0.000110800516,0.000090996095,0.00003236821,1.9758623e-7],"category_scores_gemma":[0.000008403939,0.00011798019,0.000013657466,0.00020602248,0.000021632919,0.00026897356,0.00013040914,0.00005853013,0.0000010230665],"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":[0.000008482258,0.000014205634,0.0000057557168,0.000102467595,0.00001604075,0.0000022859524,0.00019692253,0.8950722,0.043300733,0.043240435,0.00009853315,0.017941922],"study_design_scores_gemma":[0.0001957172,0.000017641534,0.00045656602,0.00003035004,0.0000039576926,0.0000070239967,0.000013383566,0.9874166,0.0004796217,0.011215595,0.000035969464,0.00012757455],"about_ca_topic_score_codex":0.0000010959245,"about_ca_topic_score_gemma":8.4558664e-8,"teacher_disagreement_score":0.41571766,"about_ca_system_score_codex":0.000024406636,"about_ca_system_score_gemma":0.000011971032,"threshold_uncertainty_score":0.48110926},"labels":[],"label_agreement":null},{"id":"W4384261910","doi":"10.48550/arxiv.2307.05616","title":"Image Reconstruction using Enhanced Vision Transformer","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"China Scholarship Council; University of Toronto","keywords":"Artificial intelligence; Computer science; Inpainting; Computer vision; Deblurring; Transformer; Noise reduction; Benchmark (surveying); Image restoration; Image (mathematics); Image processing; Engineering","score_opus":0.08413204234903895,"score_gpt":0.23984284730439287,"score_spread":0.15571080495535392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384261910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04457716,0.000020358375,0.9517505,0.000070299306,0.00069005264,0.00027647658,0.000007456053,0.0017866113,0.000821085],"genre_scores_gemma":[0.5096622,0.00017628861,0.4896395,0.000026550113,0.0000487712,0.0000011969639,0.000006073532,0.0000340303,0.00040538362],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978705,0.000086272536,0.0002538196,0.0012952775,0.00010797191,0.00038612902],"domain_scores_gemma":[0.99825984,0.000055903234,0.0002936676,0.0010282103,0.0002498521,0.000112504684],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002345427,0.000338352,0.00032782496,0.00043261133,0.00023376285,0.0002126175,0.0014552283,0.0002990754,0.000012454142],"category_scores_gemma":[0.000038870123,0.0004103389,0.00019241254,0.00084384915,0.00018632162,0.0017225713,0.0008173539,0.0006358695,0.000066708984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022895595,0.0004086316,0.00062888436,0.0016329681,0.00035993417,0.0012961196,0.0018331008,0.0883111,0.69505966,0.0499757,0.00066486,0.1596001],"study_design_scores_gemma":[0.00020509234,0.00003382757,0.00005820101,0.000341942,0.00003349351,0.000014840967,0.00003560728,0.79628354,0.020996401,0.18150131,0.000029120689,0.00046658967],"about_ca_topic_score_codex":0.000058312507,"about_ca_topic_score_gemma":0.000011275556,"teacher_disagreement_score":0.70797247,"about_ca_system_score_codex":0.00034476983,"about_ca_system_score_gemma":0.0002109327,"threshold_uncertainty_score":0.99983484},"labels":[],"label_agreement":null},{"id":"W4385333918","doi":"10.1109/isscs58449.2023.10190977","title":"Counterfactual Attention for Facial Image Super-Resolution","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Counterfactual thinking; Task (project management); Computer science; Integer (computer science); Artificial intelligence; Scale (ratio); Inference; Quality (philosophy); Image quality; Face (sociological concept); Field (mathematics); Image (mathematics); Resolution (logic); Machine learning; Computer vision; Pattern recognition (psychology); Mathematics; Psychology; Engineering; Geography","score_opus":0.026257515623349624,"score_gpt":0.31488148948986694,"score_spread":0.28862397386651734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385333918","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027464163,0.000008895681,0.9927512,0.0010386449,0.0001843288,0.00019554142,0.0000059099825,0.0023595646,0.00070951745],"genre_scores_gemma":[0.11357934,0.0000065122053,0.8842625,0.0001643729,0.00006494996,0.00008299962,0.000018749537,0.000011781599,0.0018087882],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991701,0.0000116238325,0.00013373278,0.0002808732,0.0001561013,0.0002475203],"domain_scores_gemma":[0.9995292,0.00004520099,0.00003499789,0.00023175431,0.00012682684,0.000032028616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020358866,0.000086360196,0.00007857705,0.00010739441,0.00015127276,0.00016213799,0.00040960484,0.000038840484,0.000008126415],"category_scores_gemma":[0.00008360494,0.00008106174,0.00004523055,0.00030181682,0.000039032228,0.0012069752,0.00016679186,0.000049317034,0.00014376741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029443323,0.00007789781,0.0001717354,0.00008716665,0.000014135268,0.000009195852,0.00056886656,0.000024568066,0.6647059,0.041459817,0.09203433,0.20081699],"study_design_scores_gemma":[0.00045734242,0.00015543426,0.0010899178,0.000023486937,0.000004826792,0.000009011826,0.00005368937,0.8580995,0.05122244,0.062693164,0.025861388,0.00032975798],"about_ca_topic_score_codex":0.000005496822,"about_ca_topic_score_gemma":0.0000027093442,"teacher_disagreement_score":0.85807496,"about_ca_system_score_codex":0.00004183941,"about_ca_system_score_gemma":0.000028596249,"threshold_uncertainty_score":0.3305602},"labels":[],"label_agreement":null},{"id":"W4385691485","doi":"10.1109/bsc57238.2023.10201822","title":"Rate-Distortion-Perception Tradeoff Based on the Conditional Perception Measure","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Metric (unit); Encoder; Distortion (music); Perception; Gaussian; Measure (data warehouse); Computer science; Mathematics; Algorithm; Artificial intelligence; Data compression; Computer vision; Statistics; Psychology; Data mining","score_opus":0.03163660718144654,"score_gpt":0.2786371434142464,"score_spread":0.24700053623279986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385691485","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021258495,0.0000033997667,0.98236114,0.0107833,0.00011048832,0.00017820293,0.0000035146934,0.001930607,0.0025034766],"genre_scores_gemma":[0.9033663,0.000004253019,0.09305775,0.0028250508,0.00006556875,0.000103201266,0.000042570246,0.000012756463,0.0005225053],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998775,0.0001241039,0.00016032995,0.00034429503,0.00039715436,0.00019912716],"domain_scores_gemma":[0.99920005,0.00015807914,0.000060200087,0.00042138278,0.00011607554,0.00004421042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006001197,0.00012782475,0.000081309736,0.00013819328,0.00033633088,0.00014353154,0.00050658575,0.000055795153,0.0002300003],"category_scores_gemma":[0.0001302824,0.000089598114,0.00006207736,0.0006026623,0.00007859498,0.0005603117,0.000046411165,0.00016530366,0.00056069315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007546979,0.0004187001,0.0006822498,0.00007120097,0.00003124999,0.000033428838,0.0019067802,0.004378536,0.38244668,0.12948123,0.26343772,0.21703678],"study_design_scores_gemma":[0.00019750262,0.00009597963,0.031239139,0.00003906014,0.0000050268654,0.0000036769177,0.00009991728,0.9195286,0.0025912644,0.043570157,0.0023835094,0.00024614218],"about_ca_topic_score_codex":0.0000041643093,"about_ca_topic_score_gemma":0.0000020235948,"teacher_disagreement_score":0.9151501,"about_ca_system_score_codex":0.00012635835,"about_ca_system_score_gemma":0.00006661138,"threshold_uncertainty_score":0.7206763},"labels":[],"label_agreement":null},{"id":"W4385804842","doi":"10.1109/cvprw59228.2023.00368","title":"A unified model for continuous conditional video prediction","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Autoregressive model; Interpolation (computer graphics); Artificial intelligence; Pixel; Reference frame; Frame (networking); Context (archaeology); Hidden Markov model; Context model; Pattern recognition (psychology); Computer vision; Machine learning; Image (mathematics); Mathematics; Statistics","score_opus":0.032623101850848386,"score_gpt":0.30023682143602454,"score_spread":0.26761371958517616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385804842","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001345266,0.000006663761,0.9942935,0.0009494626,0.0000634843,0.00019825347,0.000021416872,0.0031779115,0.0011547874],"genre_scores_gemma":[0.1351066,0.0000035436915,0.8600289,0.00040504948,0.000029797262,0.0001825555,0.000038486232,0.000008503314,0.0041965405],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993193,0.0000069254265,0.00012731896,0.00024363506,0.00013206988,0.0001707531],"domain_scores_gemma":[0.9995003,0.00007378972,0.000040986994,0.00020862525,0.00014081073,0.000035529858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016126789,0.00006854718,0.00007664131,0.00009819193,0.00010757445,0.000076295626,0.000325523,0.000035419354,0.0000030769468],"category_scores_gemma":[0.00008092955,0.00006495108,0.00003195396,0.00027294012,0.000029364453,0.00064439065,0.000104312574,0.000044883534,0.000021428372],"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":[0.000018241086,0.000057476216,0.00006965825,0.000046274716,0.000016371965,0.000005592884,0.00039413178,0.0062950337,0.026811527,0.79568994,0.14400601,0.026589716],"study_design_scores_gemma":[0.00011704146,0.00002119989,0.00003760522,0.000004669239,0.0000011084785,0.0000026029747,0.000003785663,0.6553706,0.0025479991,0.34108847,0.0007563337,0.000048563626],"about_ca_topic_score_codex":9.781854e-7,"about_ca_topic_score_gemma":6.746664e-7,"teacher_disagreement_score":0.64907557,"about_ca_system_score_codex":0.00002355672,"about_ca_system_score_gemma":0.00005699457,"threshold_uncertainty_score":0.2648628},"labels":[],"label_agreement":null},{"id":"W4385805019","doi":"10.1109/cvprw59228.2023.00177","title":"SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial intelligence; Residual; Computer vision; Stereo image; Frequency domain; Feature extraction; Convolution (computer science); Transformer; Image (mathematics); Algorithm; Engineering","score_opus":0.031726417406401,"score_gpt":0.3209651585220337,"score_spread":0.28923874111563275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385805019","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02222229,0.000295067,0.96820176,0.0005894451,0.00013163175,0.00014686053,0.0000017119564,0.002210686,0.0062005273],"genre_scores_gemma":[0.07483015,0.000023604367,0.92465913,0.00010579535,0.000051089104,0.000013237692,0.0000021251149,0.000018577302,0.0002962744],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998669,0.00005914029,0.0002331681,0.0004831489,0.00016342252,0.00039208436],"domain_scores_gemma":[0.99917716,0.000071024304,0.000055515582,0.0004844771,0.000117920754,0.000093913935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037819787,0.00017242518,0.00016060773,0.0002460833,0.00023494325,0.0002616033,0.00054512656,0.00006742279,0.000010433058],"category_scores_gemma":[0.000067568515,0.00016054262,0.000036400703,0.0008847995,0.000117356605,0.0018198203,0.00064379105,0.00013364866,0.00008541774],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006507745,0.00010543446,0.0014512752,0.0001741551,0.000020719555,0.0001059532,0.0032240588,0.0000053402223,0.81809735,0.115477145,0.0044460767,0.05688601],"study_design_scores_gemma":[0.0005898162,0.00013140093,0.002145269,0.00016561776,0.000010571793,0.0001365193,0.00020381657,0.39404294,0.02659959,0.57239383,0.0027578766,0.000822754],"about_ca_topic_score_codex":0.000037893413,"about_ca_topic_score_gemma":0.000014068861,"teacher_disagreement_score":0.7914977,"about_ca_system_score_codex":0.00007694389,"about_ca_system_score_gemma":0.0000828072,"threshold_uncertainty_score":0.6546738},"labels":[],"label_agreement":null},{"id":"W4386141863","doi":"10.3390/electronics12173572","title":"Sub-Pixel Convolutional Neural Network for Image Super-Resolution Reconstruction","year":2023,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Artificial intelligence; Bicubic interpolation; Convolutional neural network; Pixel; Feature (linguistics); Kernel (algebra); Image resolution; Pattern recognition (psychology); Iterative reconstruction; Interpolation (computer graphics); Convolution (computer science); Computation; Algorithm; Computer vision; Artificial neural network; Linear interpolation; Image (mathematics); Mathematics","score_opus":0.014128073814081904,"score_gpt":0.2690271570725116,"score_spread":0.2548990832584297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386141863","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064186393,0.0018092522,0.9880372,0.001321741,0.00043069068,0.00025631845,0.0000050713015,0.0016319216,0.000089124296],"genre_scores_gemma":[0.03902689,0.00035721785,0.95932144,0.00026400812,0.00045172704,0.00018911422,0.00006500531,0.00003514329,0.00028943887],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851143,0.00003884558,0.00020877155,0.00039184853,0.00017718019,0.00067193765],"domain_scores_gemma":[0.99926937,0.00010464116,0.000090263304,0.00029958226,0.00018451686,0.000051628398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037136968,0.00013937062,0.00013048897,0.00009731733,0.0003380132,0.00011768093,0.00045313183,0.000075277276,0.0000023938685],"category_scores_gemma":[0.00010727255,0.00015291592,0.00007392951,0.00071102683,0.00007819169,0.00092986267,0.0001229984,0.00020482649,0.000027396467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008128656,0.0000645478,0.00032719513,0.00007792481,0.000045093282,0.000008738254,0.00014651932,0.0029666824,0.23680894,0.21103658,0.07127047,0.47716603],"study_design_scores_gemma":[0.00022376336,0.00013134562,0.00020314897,0.000014391418,0.0000054207794,0.00008006224,0.0000031371521,0.79116595,0.008088909,0.17995158,0.019929282,0.00020301972],"about_ca_topic_score_codex":9.569648e-7,"about_ca_topic_score_gemma":0.000009169305,"teacher_disagreement_score":0.78819925,"about_ca_system_score_codex":0.0002055705,"about_ca_system_score_gemma":0.00019510559,"threshold_uncertainty_score":0.62357306},"labels":[],"label_agreement":null},{"id":"W4386162060","doi":"10.3390/rs15174180","title":"Super-Resolution Reconstruction of Remote Sensing Data Based on Multiple Satellite Sources for Forest Fire Smoke Segmentation","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Energy, Northern Development and Mines","funders":"National Natural Science Foundation of China","keywords":"Remote sensing; Smoke; Segmentation; Multispectral image; Image resolution; Environmental science; Computer science; Satellite; Artificial intelligence; Geography; Meteorology","score_opus":0.06300925239240482,"score_gpt":0.3126941577497477,"score_spread":0.24968490535734286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386162060","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.095592424,0.00006203359,0.9023326,0.00047664123,0.0002908054,0.00037459668,0.000013405177,0.0008102483,0.000047228714],"genre_scores_gemma":[0.2215533,0.00003938343,0.77806574,0.00009380004,0.0000803203,1.862297e-8,0.00012157302,0.000029201785,0.0000166347],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981003,0.00009490416,0.0004220547,0.00069297577,0.00032351477,0.00036629435],"domain_scores_gemma":[0.9977705,0.0005566165,0.0003099831,0.0010676545,0.00023780424,0.000057471476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000666325,0.000208674,0.00024195874,0.00028566533,0.00031352794,0.0001330047,0.0003774611,0.00010643954,2.5165835e-7],"category_scores_gemma":[0.0009545081,0.00022390741,0.0000693912,0.00071806426,0.0000965272,0.0007806297,0.00021524406,0.00014459383,0.000003546117],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032970165,0.000003888649,0.000029466117,0.00008667379,0.0000064179253,0.000005580214,0.00018190478,0.0022720434,0.06096832,0.000006076028,0.000035166606,0.9363715],"study_design_scores_gemma":[0.000355595,0.00006388385,0.0001598272,0.0005340925,0.000013936901,0.000037385835,0.00008520441,0.9287916,0.06561919,0.0037788795,0.00035285208,0.00020759486],"about_ca_topic_score_codex":0.00020313656,"about_ca_topic_score_gemma":0.000059017682,"teacher_disagreement_score":0.9361639,"about_ca_system_score_codex":0.000118941214,"about_ca_system_score_gemma":0.000079685684,"threshold_uncertainty_score":0.91306794},"labels":[],"label_agreement":null},{"id":"W4386243250","doi":"10.1109/infocom53939.2023.10228906","title":"Collaborative Streaming and Super Resolution Adaptation for Mobile Immersive Videos","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Quality of experience; Viewport; Multimedia; Video quality; Mobile device; Bandwidth (computing); Video processing; Real-time computing; Computer network; Artificial intelligence; Quality of service","score_opus":0.01591620042748809,"score_gpt":0.29756672408939194,"score_spread":0.28165052366190385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386243250","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00791847,0.000105082014,0.9905988,0.0003191267,0.000036830254,0.00038555308,0.0000054663046,0.0004511069,0.00017959274],"genre_scores_gemma":[0.2460585,0.00004696318,0.7530673,0.000055025677,0.00001612927,0.00026772887,0.00000818424,0.000007322677,0.0004728095],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940157,0.000017193484,0.00009527824,0.00025036416,0.00008721471,0.00014840723],"domain_scores_gemma":[0.99947464,0.00011027526,0.000041445503,0.00013307942,0.00021207146,0.00002849233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000108407556,0.000069076574,0.0000700519,0.00009992535,0.00016500878,0.000077990815,0.00013350371,0.000029208806,0.0000013043882],"category_scores_gemma":[0.00008134623,0.000065187494,0.000012262966,0.00053509074,0.000034606455,0.0009371444,0.000100466066,0.00003215062,0.0000047432336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010882203,0.000084702035,0.00028494053,0.00021267965,0.00004866979,0.000018380328,0.028681917,0.0017422257,0.57545096,0.07573576,0.01068764,0.30694333],"study_design_scores_gemma":[0.00036557068,0.00019514054,0.00054137816,0.000031612697,0.000004618821,0.0000036715267,0.0032256907,0.8756457,0.09451862,0.02373263,0.0015392159,0.00019615854],"about_ca_topic_score_codex":0.000008127639,"about_ca_topic_score_gemma":0.000007192593,"teacher_disagreement_score":0.87390345,"about_ca_system_score_codex":0.000035048262,"about_ca_system_score_gemma":0.000042910073,"threshold_uncertainty_score":0.2658269},"labels":[],"label_agreement":null},{"id":"W4386245195","doi":"10.1109/infocom53939.2023.10228933","title":"AccDecoder: Accelerated Decoding for Neural-enhanced Video Analytics","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"CODE; National Natural Science Foundation of China","keywords":"Computer science; Video quality; Analytics; Artificial intelligence; Inference; Latency (audio); Artificial neural network; Decoding methods; Real-time computing; Computer vision; Machine learning; Data mining; Algorithm; Telecommunications","score_opus":0.07668023007045321,"score_gpt":0.3578334811042763,"score_spread":0.2811532510338231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386245195","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018331362,0.000025294612,0.99067205,0.0015214356,0.00016567911,0.00024370478,0.000001807629,0.004222779,0.0013141222],"genre_scores_gemma":[0.22337215,0.000027703381,0.77435786,0.00069254317,0.00004493967,0.00008386371,0.0000067234855,0.000021361568,0.0013928601],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986074,0.00001679589,0.00026973677,0.00048892567,0.00017307884,0.00044407666],"domain_scores_gemma":[0.998828,0.00023076593,0.000099696386,0.0005227611,0.00024036638,0.00007840275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025783776,0.00016234037,0.00018486807,0.0002333068,0.00021512939,0.00037116918,0.0010676391,0.00005920248,0.00001184498],"category_scores_gemma":[0.000290305,0.00014922995,0.00006970029,0.0014460958,0.000028918634,0.0012956279,0.00037362784,0.00009924796,0.00005039095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023083969,0.000060985258,0.00012840892,0.00010721532,0.000040628704,0.000020845753,0.00044369805,0.0022825603,0.23015648,0.021949483,0.048867796,0.6959188],"study_design_scores_gemma":[0.00016673315,0.000036323894,0.000048546943,0.000012851454,0.0000032698458,0.0000023960342,0.000011608404,0.8228426,0.14730503,0.028297335,0.0010915766,0.00018172908],"about_ca_topic_score_codex":0.0000024052322,"about_ca_topic_score_gemma":0.000004649021,"teacher_disagreement_score":0.82056004,"about_ca_system_score_codex":0.00004344388,"about_ca_system_score_gemma":0.000053113676,"threshold_uncertainty_score":0.6085421},"labels":[],"label_agreement":null},{"id":"W4386256500","doi":"10.32920/24050769","title":"Progressively Growing of Least Squares Generative Adversarial Networks","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Generative grammar; Adversarial system; Computer science; Focus (optics); Set (abstract data type); Architecture; Variety (cybernetics); Function (biology); Stability (learning theory); Artificial intelligence; Image (mathematics); Variation (astronomy); Image translation; Domain (mathematical analysis); Network architecture; State (computer science); Machine learning; Mathematical optimization; Algorithm; Mathematics; Computer security; Geography","score_opus":0.03844370430106291,"score_gpt":0.3111744307346231,"score_spread":0.27273072643356017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386256500","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009846228,0.00043025278,0.9945718,0.00056058,0.001022651,0.00043846224,0.000004391031,0.0020144358,0.000858948],"genre_scores_gemma":[0.065555975,0.000055387998,0.9332208,0.00011637101,0.0003356203,0.00014940155,0.0000173436,0.00004256618,0.0005065175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976478,0.00010555958,0.00051215745,0.0009231788,0.00042629882,0.00038495768],"domain_scores_gemma":[0.9979356,0.00011512653,0.0005499707,0.00092517614,0.00039708434,0.000077072276],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003337581,0.00035950445,0.00048667841,0.00021690073,0.00011435215,0.00025498096,0.0021341739,0.0002936377,0.0000055099927],"category_scores_gemma":[0.00015704727,0.00032988537,0.00016847288,0.00039231742,0.00016890476,0.00081525167,0.0047329166,0.00067913986,0.0000068328814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001643152,0.0006354658,0.0018446707,0.0019647921,0.0008554154,0.0005161163,0.005318093,0.20253165,0.011371908,0.3426323,0.023198456,0.4089668],"study_design_scores_gemma":[0.00024030365,0.00010208313,0.00015435545,0.00078690954,0.000023529392,0.0000058129813,0.00004493235,0.89390224,0.02041341,0.08341247,0.00027410293,0.0006398421],"about_ca_topic_score_codex":0.000043862692,"about_ca_topic_score_gemma":0.000007069347,"teacher_disagreement_score":0.6913706,"about_ca_system_score_codex":0.00007881459,"about_ca_system_score_gemma":0.00035202864,"threshold_uncertainty_score":0.9999153},"labels":[],"label_agreement":null},{"id":"W4386257185","doi":"10.32920/24050769.v1","title":"Progressively Growing of Least Squares Generative Adversarial Networks","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Generative grammar; Adversarial system; Focus (optics); Computer science; Set (abstract data type); Variety (cybernetics); Function (biology); Architecture; Stability (learning theory); Artificial intelligence; Image translation; Variation (astronomy); Image (mathematics); Domain (mathematical analysis); Network architecture; State (computer science); Machine learning; Mathematical optimization; Algorithm; Mathematics; Computer security; Geography","score_opus":0.03844370430106291,"score_gpt":0.3111744307346231,"score_spread":0.27273072643356017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386257185","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009846228,0.00043025278,0.9945718,0.00056058,0.001022651,0.00043846224,0.000004391031,0.0020144358,0.000858948],"genre_scores_gemma":[0.065555975,0.000055387998,0.9332208,0.00011637101,0.0003356203,0.00014940155,0.0000173436,0.00004256618,0.0005065175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976478,0.00010555958,0.00051215745,0.0009231788,0.00042629882,0.00038495768],"domain_scores_gemma":[0.9979356,0.00011512653,0.0005499707,0.00092517614,0.00039708434,0.000077072276],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003337581,0.00035950445,0.00048667841,0.00021690073,0.00011435215,0.00025498096,0.0021341739,0.0002936377,0.0000055099927],"category_scores_gemma":[0.00015704727,0.00032988537,0.00016847288,0.00039231742,0.00016890476,0.00081525167,0.0047329166,0.00067913986,0.0000068328814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001643152,0.0006354658,0.0018446707,0.0019647921,0.0008554154,0.0005161163,0.005318093,0.20253165,0.011371908,0.3426323,0.023198456,0.4089668],"study_design_scores_gemma":[0.00024030365,0.00010208313,0.00015435545,0.00078690954,0.000023529392,0.0000058129813,0.00004493235,0.89390224,0.02041341,0.08341247,0.00027410293,0.0006398421],"about_ca_topic_score_codex":0.000043862692,"about_ca_topic_score_gemma":0.000007069347,"teacher_disagreement_score":0.6913706,"about_ca_system_score_codex":0.00007881459,"about_ca_system_score_gemma":0.00035202864,"threshold_uncertainty_score":0.9999153},"labels":[],"label_agreement":null},{"id":"W4386260373","doi":"10.1109/crv60082.2023.00010","title":"Towards Low-Cost Learning-based Camera ISP via Unrolled Optimization","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Pipeline (software); Overhead (engineering); Convolutional neural network; Artificial intelligence; Computation; Deep learning; Pipeline transport; Image (mathematics); Optimization problem; Computer engineering; Computer vision; Algorithm; Engineering","score_opus":0.012496114074707266,"score_gpt":0.27920350351455187,"score_spread":0.2667073894398446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386260373","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000735514,0.000014654362,0.9908225,0.0015630223,0.000106432,0.00020440658,4.22297e-7,0.004876302,0.002338727],"genre_scores_gemma":[0.08364841,0.000013038566,0.9138913,0.0007590101,0.000028253206,0.00007116383,0.000016073307,0.000024158137,0.0015485801],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987317,0.000057768884,0.00020210746,0.00040981534,0.0002852701,0.00031334226],"domain_scores_gemma":[0.9991774,0.00006404267,0.00009525508,0.00040692024,0.0001799574,0.00007643637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000279541,0.00015264908,0.00015501255,0.00023621677,0.00018740678,0.00019972435,0.00069082476,0.000063057894,0.00006358464],"category_scores_gemma":[0.00018843898,0.000140767,0.00005089851,0.0013932681,0.000045173758,0.000663982,0.00023070745,0.00017626511,0.00015468548],"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":[0.000016732214,0.00007037514,0.00021345659,0.000047388094,0.000009815511,0.000039342925,0.00025614977,0.82023764,0.0028455916,0.0010458701,0.003744015,0.17147364],"study_design_scores_gemma":[0.00027176863,0.00003818859,0.00005405231,0.000021048572,0.0000020203472,0.0000022538784,0.0000060346038,0.9786588,0.018028595,0.0015504641,0.0011827264,0.00018405997],"about_ca_topic_score_codex":0.000017800106,"about_ca_topic_score_gemma":0.0000016694627,"teacher_disagreement_score":0.17128958,"about_ca_system_score_codex":0.00006076022,"about_ca_system_score_gemma":0.00012666218,"threshold_uncertainty_score":0.57403123},"labels":[],"label_agreement":null},{"id":"W4386598528","doi":"10.1109/icip49359.2023.10223143","title":"Encoding-Aware Deep Video Super-Resolution Framework","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BlueDot (Canada)","funders":"Ministry of Science and ICT, South Korea; National IT Industry Promotion Agency","keywords":"Codec; Computer science; Video quality; Encoding (memory); Data compression; Bandwidth (computing); Artificial intelligence; Image quality; Real-time computing; Computer vision; Image (mathematics); Computer network; Computer hardware; Engineering","score_opus":0.021994497440367664,"score_gpt":0.30070929763492543,"score_spread":0.27871480019455774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386598528","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024252814,0.00006647972,0.98939973,0.0023204237,0.00020586867,0.00008975766,4.6298794e-7,0.0053644343,0.002310308],"genre_scores_gemma":[0.16248885,0.000032983196,0.8360608,0.00068700843,0.00006128863,0.000032150074,0.0000023149091,0.000012958825,0.00062165083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879366,0.000027942955,0.00016553747,0.00040368523,0.00025538474,0.0003537695],"domain_scores_gemma":[0.9990611,0.00014436335,0.000041324976,0.00058474654,0.00009653206,0.00007193971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022610076,0.00012343864,0.0001108115,0.00016449227,0.0001857796,0.0001815243,0.0009166439,0.00008948974,0.000036366502],"category_scores_gemma":[0.00025727748,0.000113960625,0.000043695145,0.0011597378,0.000046730464,0.0009818388,0.0005006828,0.0001863478,0.00040921167],"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":[0.000010004589,0.000105992665,0.0014027497,0.000109065666,0.000022215838,0.00019432284,0.002290086,0.00040293293,0.017552821,0.5739859,0.0357876,0.36813632],"study_design_scores_gemma":[0.000065315,0.000043811353,0.0003843616,0.000062931416,0.00000209084,0.000016199423,0.000042291147,0.6139697,0.011480916,0.3674941,0.0061743683,0.00026392567],"about_ca_topic_score_codex":0.000008588795,"about_ca_topic_score_gemma":0.000003782213,"teacher_disagreement_score":0.61356676,"about_ca_system_score_codex":0.00005289955,"about_ca_system_score_gemma":0.00003609427,"threshold_uncertainty_score":0.5259725},"labels":[],"label_agreement":null},{"id":"W4387415306","doi":"10.1109/tccn.2023.3320879","title":"Adaptive Network Configuration for Efficient and Accurate Neural Video Inference","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Fundamental Research Funds for the Central Universities; Young Elite Scientists Sponsorship Program by Tianjin; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial neural network; Inference; Artificial intelligence; Computer network","score_opus":0.08245911716342078,"score_gpt":0.3443223480146576,"score_spread":0.26186323085123686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387415306","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006381233,0.0008383005,0.996536,0.0006180002,0.00017005477,0.0004979138,0.000013966317,0.00048619666,0.00020141111],"genre_scores_gemma":[0.9240161,0.0023529003,0.07299652,0.00025545055,0.00003923666,0.00028719133,0.000008991142,0.000014649708,0.000028920762],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989399,0.000121500794,0.00022529408,0.0003395582,0.00009878898,0.00027496647],"domain_scores_gemma":[0.9971664,0.0019599379,0.00012255971,0.0004332197,0.00025100887,0.000066890796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032138018,0.00016054306,0.00015482084,0.00013141302,0.0011768248,0.0002095344,0.00038652186,0.000058617712,9.085188e-7],"category_scores_gemma":[0.000018110622,0.00016585403,0.000038360566,0.000687378,0.0002141317,0.00036175107,0.000032264677,0.00024941727,0.0000031899551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044603563,0.00005026803,0.0000139031235,0.000015879483,0.0000313139,9.686714e-7,0.0006451536,0.0052292095,0.00026822774,0.0026349484,0.00004882984,0.9910167],"study_design_scores_gemma":[0.0003055677,0.00015017438,0.00010317247,0.0002548223,0.000024980069,0.0000052112086,0.00010867523,0.9927809,0.0005618771,0.005065519,0.00044877673,0.00019032677],"about_ca_topic_score_codex":0.0000054389934,"about_ca_topic_score_gemma":0.000022344711,"teacher_disagreement_score":0.99082637,"about_ca_system_score_codex":0.000022076887,"about_ca_system_score_gemma":0.000034520606,"threshold_uncertainty_score":0.9051304},"labels":[],"label_agreement":null},{"id":"W4387735143","doi":"10.1109/ic3ina60834.2023.10285777","title":"The Impact of Downsampling Methods on Face Recognition in Electronic Identity Card","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Upsampling; Bicubic interpolation; Bilinear interpolation; Computer science; Artificial intelligence; Facial recognition system; Lanczos resampling; Interpolation (computer graphics); Stairstep interpolation; Face (sociological concept); Pattern recognition (psychology); Computer vision; Mathematics; Linear interpolation; Image (mathematics)","score_opus":0.057860466290646076,"score_gpt":0.45468580615212767,"score_spread":0.3968253398614816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387735143","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01655374,0.00007480102,0.9820973,0.00013933556,0.000032780103,0.000105474945,5.836806e-7,0.00039362203,0.0006023553],"genre_scores_gemma":[0.41717404,0.00018058644,0.5825207,0.000023453485,0.0000085077545,0.00002233573,0.0000013733292,0.0000070815395,0.000061910156],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991175,0.0001169556,0.00016493177,0.00019741358,0.00013274151,0.00027046533],"domain_scores_gemma":[0.99916667,0.000360348,0.0000755763,0.00031512236,0.0000635889,0.00001869094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013391526,0.00007503004,0.00009979203,0.00013282795,0.00007607752,0.0000885296,0.0005579829,0.000028564657,0.0000017761122],"category_scores_gemma":[0.00032549942,0.000049579336,0.0000579468,0.0010452018,0.000027861333,0.0006483969,0.00015748882,0.00016065905,0.00001619773],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015010029,0.00003114528,0.00028361575,0.000012401666,0.00001585655,0.0000020847253,0.00037611282,0.00079657615,0.0523194,0.010110074,0.0002817697,0.93575597],"study_design_scores_gemma":[0.00011542151,0.00017102562,0.0035362788,0.000043214706,0.0000017598863,0.0000030337428,0.000041937248,0.15737422,0.11034088,0.72817785,0.000057776408,0.00013662095],"about_ca_topic_score_codex":0.00012405794,"about_ca_topic_score_gemma":0.000030500138,"teacher_disagreement_score":0.93561935,"about_ca_system_score_codex":0.00013552376,"about_ca_system_score_gemma":0.000078079436,"threshold_uncertainty_score":0.20217867},"labels":[],"label_agreement":null},{"id":"W4387802160","doi":"10.1109/igarss52108.2023.10282510","title":"Enhancing Spatial Resolution of Building Datasets Using Transformer-Based Single-Image Super-Resolution","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Computer science; Image resolution; Artificial intelligence; Bicubic interpolation; Residual; Convolutional neural network; Pattern recognition (psychology); Feature extraction; Transformer; Computer vision; Algorithm; Linear interpolation","score_opus":0.03917950623569714,"score_gpt":0.3163512290848572,"score_spread":0.27717172284916003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387802160","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009755043,0.00003895852,0.9882077,0.0002623752,0.00013186534,0.00019684863,0.000031407,0.0012416539,0.00013414263],"genre_scores_gemma":[0.37431356,0.0000029605812,0.6255415,0.000045168326,0.000027874641,0.000006384425,0.000041596515,0.000014472095,0.000006493138],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980929,0.00007367942,0.00045575286,0.0004924891,0.00042186573,0.00046332265],"domain_scores_gemma":[0.99896455,0.00011337913,0.00014917416,0.00056254386,0.00013925432,0.00007107197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005979527,0.00018971576,0.00022117709,0.00041010947,0.00022518626,0.00011308056,0.0006575045,0.00008370907,0.000009644265],"category_scores_gemma":[0.00018687634,0.00019487785,0.000072098046,0.001138257,0.0001140309,0.0017476262,0.0001469921,0.00014342293,0.000007037981],"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":[0.000010961848,0.00004366408,0.000011993351,0.00007413794,0.0000034382533,0.000007722139,0.00009425151,0.0008252589,0.9866015,0.00060678535,0.00012643056,0.011593882],"study_design_scores_gemma":[0.00014306144,0.000055849385,0.000022751416,0.00010848673,0.000005332903,0.000004453918,0.000010831569,0.48589188,0.5123197,0.0011611341,0.00014891165,0.00012764511],"about_ca_topic_score_codex":0.00028585756,"about_ca_topic_score_gemma":0.00006479632,"teacher_disagreement_score":0.48506662,"about_ca_system_score_codex":0.00015549021,"about_ca_system_score_gemma":0.0001409117,"threshold_uncertainty_score":0.7946888},"labels":[],"label_agreement":null},{"id":"W4387805473","doi":"10.1109/lsp.2023.3326387","title":"DPAN: A Deep Light-Weight Attention-Based Image Super Resolution Network Using Multi-Dimensional Filter Design Technique","year":2023,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Residual; Block (permutation group theory); Filter (signal processing); Computer vision; Feature extraction; Image resolution; Pattern recognition (psychology); Filter bank; Algorithm; Mathematics","score_opus":0.03529100183999435,"score_gpt":0.2791157664602518,"score_spread":0.24382476462025748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387805473","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018070797,0.00019868158,0.99192065,0.0023359803,0.00026075522,0.0006613697,0.0000026756582,0.0028001175,0.000012681226],"genre_scores_gemma":[0.07033981,0.0000020259838,0.9260767,0.0028838909,0.00032196337,0.0002272623,0.00001474154,0.00009465789,0.000038955503],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99604434,0.00031814305,0.0006297962,0.0011328247,0.0007697353,0.0011051904],"domain_scores_gemma":[0.99834526,0.00020238987,0.00035382403,0.0005939818,0.0003553412,0.00014921119],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011324353,0.000516208,0.00038791884,0.00058656285,0.0009482044,0.00058895483,0.0011419144,0.00020438134,0.000010469497],"category_scores_gemma":[0.000054792603,0.0005085297,0.0001729041,0.0022301623,0.000245312,0.0021515426,0.00023058307,0.0005376851,0.00005696283],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035466648,0.00006953947,0.00006667102,0.00010854359,0.000014615678,0.00016784482,0.00013025638,0.031232564,0.9596681,0.000011406603,0.005138977,0.0033560016],"study_design_scores_gemma":[0.00040037723,0.000050564337,0.000066665445,0.0005701987,0.000023400551,0.00005404676,0.0000038466023,0.8618291,0.13500454,0.001114281,0.0002802587,0.00060269533],"about_ca_topic_score_codex":0.000013989784,"about_ca_topic_score_gemma":0.0000011145988,"teacher_disagreement_score":0.83059657,"about_ca_system_score_codex":0.00027066088,"about_ca_system_score_gemma":0.00033455985,"threshold_uncertainty_score":0.9997366},"labels":[],"label_agreement":null},{"id":"W4387843838","doi":"10.3390/rs15205062","title":"A Review of GAN-Based Super-Resolution Reconstruction for Optical Remote Sensing Images","year":2023,"lang":"en","type":"review","venue":"Remote Sensing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Science Foundation of Shandong Province","keywords":"Computer science; Adversarial system; Generative grammar; Generative adversarial network; Artificial intelligence; Superresolution; Iterative reconstruction; Computer vision; Image (mathematics)","score_opus":0.07273384696452204,"score_gpt":0.36972109302331985,"score_spread":0.2969872460587978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387843838","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.1220694e-8,0.4901942,0.50791085,0.00014499012,0.0002628794,0.00076131325,0.000006541006,0.00061623147,0.000102974765],"genre_scores_gemma":[1.3129464e-7,0.49961075,0.500078,0.00010281714,0.000091735936,5.8029872e-8,0.000022388596,0.00007122313,0.000022861923],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9959513,0.00029926933,0.0015191486,0.0011471793,0.000435403,0.00064771296],"domain_scores_gemma":[0.9956826,0.0009252841,0.0011649305,0.0013479671,0.00077237346,0.00010681937],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015275197,0.00064707507,0.0022630964,0.00055992784,0.00023231942,0.00015160223,0.00059496117,0.0003892109,6.37464e-7],"category_scores_gemma":[0.0024364118,0.00062312384,0.0008116807,0.0014691707,0.00022561292,0.00039752253,0.00016602271,0.0005817874,0.000014708776],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022879992,0.0000027368417,7.1225875e-10,0.10459606,0.000026058146,0.000018590508,0.000005695918,0.0000027956285,0.00017839835,0.000014737816,0.00021645076,0.8949362],"study_design_scores_gemma":[0.00010452082,0.000045853838,9.250376e-9,0.42471087,0.00034525653,0.00076016114,0.0000018735462,0.42276925,0.0008247674,0.002686404,0.14721574,0.00053527736],"about_ca_topic_score_codex":0.000027331036,"about_ca_topic_score_gemma":0.000002028008,"teacher_disagreement_score":0.8944009,"about_ca_system_score_codex":0.00041460257,"about_ca_system_score_gemma":0.00074872724,"threshold_uncertainty_score":0.999622},"labels":[],"label_agreement":null},{"id":"W4387951312","doi":"10.1109/ccece58730.2023.10289047","title":"Replacing Averaging with More Powerful Self-Attention Mechanism for Multi-Image Super-Resolution","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computation; Baseline (sea); Computer science; Image (mathematics); Artificial intelligence; Field (mathematics); Resolution (logic); State (computer science); Algorithm; Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.021948048930209362,"score_gpt":0.29919219879136727,"score_spread":0.2772441498611579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387951312","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033839666,0.000008208588,0.98924154,0.0012260275,0.00010477305,0.00040346576,0.0000016683397,0.0053872922,0.00024304652],"genre_scores_gemma":[0.05825746,0.0000061396,0.9404183,0.0002778809,0.00002503148,0.00007734872,0.000009892144,0.000027989903,0.00089993654],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985473,0.000023341692,0.00020162865,0.00057575497,0.0002488307,0.00040315246],"domain_scores_gemma":[0.99901634,0.00004722785,0.00009034267,0.000484968,0.00030133504,0.000059776863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036577642,0.00017219562,0.00014181621,0.00022011927,0.00026239734,0.0002335985,0.00046023884,0.00005342443,0.0000024212181],"category_scores_gemma":[0.00007935495,0.00014924127,0.00005109716,0.00060499914,0.000027100308,0.0016557302,0.00022925487,0.00010949005,0.000031326006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007675782,0.00034572696,0.0006152762,0.0005701103,0.00010147975,0.00014522643,0.005533519,0.00022160976,0.7640615,0.1991039,0.006369068,0.022855837],"study_design_scores_gemma":[0.00045871147,0.00008898788,0.0001498597,0.00007089118,0.000008061347,0.00003056847,0.00012831675,0.93165076,0.056728963,0.010281208,0.00016160568,0.00024209324],"about_ca_topic_score_codex":0.000012701134,"about_ca_topic_score_gemma":0.000004742338,"teacher_disagreement_score":0.93142915,"about_ca_system_score_codex":0.0000956937,"about_ca_system_score_gemma":0.000048424863,"threshold_uncertainty_score":0.6085883},"labels":[],"label_agreement":null},{"id":"W4388020864","doi":"10.18280/ts.400539","title":"Automatic Depth Estimation and Background Blurring of Animated Scenes Based on Deep Learning","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"College of Humanities and Social Sciences, United Arab Emirates University","keywords":"Artificial intelligence; Computer science; Deep learning; Computer vision; Estimation; Computer graphics (images); Pattern recognition (psychology); Engineering","score_opus":0.024787381441049115,"score_gpt":0.2888368605912724,"score_spread":0.26404947915022325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388020864","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18034542,0.00002518946,0.8183814,0.00008985205,0.000017671957,0.00012251851,3.756702e-7,0.00090693147,0.00011063198],"genre_scores_gemma":[0.6823064,0.0000026679888,0.31761214,0.000039269053,0.000006477359,0.000015593087,0.000004112043,0.000008793905,0.0000045535376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989726,0.000056128927,0.00024026881,0.00025461923,0.00028238146,0.000194034],"domain_scores_gemma":[0.99943155,0.00018355278,0.00014075484,0.00014403902,0.00005715779,0.000042930296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003665407,0.00012375954,0.00014162644,0.00021907486,0.00012893419,0.00010954545,0.00024118643,0.00002905236,0.000016306434],"category_scores_gemma":[0.00005602982,0.00012042772,0.000023883089,0.00047334516,0.000045964563,0.0004523759,0.00008287374,0.000086871914,0.000008841885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023784702,0.00013882125,0.004215663,0.0005478021,0.000021728636,0.000024338562,0.0009198414,0.049629055,0.07088939,0.0029869224,0.000055730503,0.87054694],"study_design_scores_gemma":[0.00026793746,0.00018472844,0.011961797,0.0001881971,0.0000060775383,0.0000024809203,0.000027370223,0.973042,0.012975355,0.0012138549,0.000013224841,0.00011694322],"about_ca_topic_score_codex":0.000003173816,"about_ca_topic_score_gemma":0.0000011881024,"teacher_disagreement_score":0.923413,"about_ca_system_score_codex":0.00003467648,"about_ca_system_score_gemma":0.00002355086,"threshold_uncertainty_score":0.49109},"labels":[],"label_agreement":null},{"id":"W4388399456","doi":"10.20944/preprints202311.0231.v1","title":"Single Image Super Resolution using Deep Residual Learning","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Autoencoder; Residual; Artificial intelligence; Computer science; Deep learning; Convolution (computer science); Interpolation (computer graphics); Transpose; Sampling (signal processing); Image quality; Image (mathematics); Pattern recognition (psychology); Computer vision; Machine learning; Algorithm; Artificial neural network; Filter (signal processing)","score_opus":0.20004846864787354,"score_gpt":0.3832797132569491,"score_spread":0.18323124460907556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388399456","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10184849,0.00021449753,0.8894557,0.00057292014,0.00069748965,0.00047145426,0.0000035635492,0.004970902,0.0017650059],"genre_scores_gemma":[0.351262,0.000076522636,0.64693487,0.00007868111,0.00028450225,0.000102809514,0.000027602782,0.00012986113,0.0011031427],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99510455,0.00039697156,0.00076341757,0.0021596665,0.0007483276,0.0008270745],"domain_scores_gemma":[0.9961986,0.00016452145,0.00056576246,0.002425111,0.0004756703,0.00017033379],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015054794,0.0005673392,0.00054935034,0.0004967468,0.0004489014,0.00032656666,0.0028043133,0.00047869174,0.000052559884],"category_scores_gemma":[0.0016507949,0.0006677144,0.0002038033,0.0005892532,0.0002303393,0.0012094054,0.012826891,0.0020970919,0.00080994616],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056334717,0.00034945252,0.06261597,0.00077885657,0.00015431001,0.000325428,0.0048458283,0.018326439,0.9024904,0.0015976339,0.000226838,0.008232542],"study_design_scores_gemma":[0.00046565232,0.00007791214,0.02298704,0.0014926464,0.000091203874,0.00009932438,0.0001598728,0.48110655,0.38720822,0.101172656,0.0028793232,0.0022596042],"about_ca_topic_score_codex":0.00018620794,"about_ca_topic_score_gemma":0.000012791805,"teacher_disagreement_score":0.51528215,"about_ca_system_score_codex":0.0006048171,"about_ca_system_score_gemma":0.0003013709,"threshold_uncertainty_score":0.99996805},"labels":[],"label_agreement":null},{"id":"W4388525306","doi":"10.3390/jimaging9110246","title":"OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution","year":2023,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Computer science; Artificial intelligence; Computer vision; RGB color model; Image (mathematics); Image resolution; Coherence (philosophical gambling strategy); Point (geometry); Representation (politics); Mathematics","score_opus":0.021537907967740917,"score_gpt":0.30048853489996297,"score_spread":0.27895062693222206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388525306","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00133786,0.0001940848,0.9918655,0.0053725564,0.000379908,0.00012726622,8.811956e-7,0.00033678848,0.0003851308],"genre_scores_gemma":[0.36480978,0.00006907624,0.63371015,0.0008365551,0.0003889731,0.000011373732,0.0000013992778,0.000034965986,0.00013774564],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851596,0.00004720869,0.00043886012,0.00024946904,0.00037496988,0.00037354257],"domain_scores_gemma":[0.99866617,0.00019447574,0.00038849498,0.00029269772,0.00036847842,0.00008970948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074331096,0.00015490508,0.00022873822,0.00044597336,0.00020766947,0.00023740796,0.00069409946,0.0000351106,0.0000010023069],"category_scores_gemma":[0.0003477427,0.00014038455,0.00015351981,0.0005164497,0.0000756124,0.002051543,0.00016321128,0.00030183507,0.00000941795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019459854,0.00017211789,0.00069467473,0.00024121146,0.000057456033,0.0011310743,0.0018054357,0.0022771698,0.32482296,0.008498573,0.08067489,0.5794298],"study_design_scores_gemma":[0.0010502572,0.00024119242,0.0004166548,0.0009227885,0.000019524834,0.0010905184,0.00021008308,0.89412177,0.034447003,0.05521507,0.011886869,0.00037825],"about_ca_topic_score_codex":0.0000023891175,"about_ca_topic_score_gemma":2.7513582e-7,"teacher_disagreement_score":0.89184463,"about_ca_system_score_codex":0.0002040193,"about_ca_system_score_gemma":0.00011026278,"threshold_uncertainty_score":0.57247156},"labels":[],"label_agreement":null},{"id":"W4388579611","doi":"10.1109/jiot.2023.3331699","title":"AdaDSR: Adaptive Configuration Optimization for Neural Enhanced Video Analytics Streaming","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Upsampling; Video quality; Analytics; Overhead (engineering); Bandwidth (computing); Real-time computing; Video processing; Artificial neural network; Artificial intelligence; Data mining; Computer network","score_opus":0.02948241953338656,"score_gpt":0.30089942321236174,"score_spread":0.2714170036789752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388579611","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004095951,0.00003956938,0.99445355,0.00031584076,0.00050783745,0.0001364493,0.0000014119573,0.00027150117,0.00017790789],"genre_scores_gemma":[0.47413948,0.00001893822,0.52545875,0.000099923316,0.00006931666,0.0000068868753,0.0000021188603,0.000011519753,0.00019304788],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873155,0.00003952137,0.00046351095,0.00023277113,0.00029380343,0.0002388368],"domain_scores_gemma":[0.99838626,0.00015978466,0.00062174676,0.000180136,0.00058876746,0.00006329851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044785775,0.00013962053,0.00020104503,0.00028841128,0.000096217555,0.00025553184,0.0007411498,0.00006183388,0.0000070382644],"category_scores_gemma":[0.0002788414,0.00013284024,0.00010267541,0.00035608883,0.000049832466,0.0021742594,0.00008192654,0.00023198544,0.0000026292605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000290144,0.00013104331,0.00004515394,0.00014818551,0.00020159308,0.000051509003,0.010837122,0.27771965,0.2445176,0.0048043877,0.00862478,0.45262882],"study_design_scores_gemma":[0.00020567061,0.00020317202,0.0000071866366,0.00013752062,0.000009985321,0.000033274908,0.000056570418,0.7845399,0.20877524,0.005870675,0.000056388544,0.00010445051],"about_ca_topic_score_codex":0.0000071409354,"about_ca_topic_score_gemma":7.2890214e-7,"teacher_disagreement_score":0.5068202,"about_ca_system_score_codex":0.00008511425,"about_ca_system_score_gemma":0.00006574225,"threshold_uncertainty_score":0.5417068},"labels":[],"label_agreement":null},{"id":"W4389009069","doi":"10.1007/978-981-99-8388-9_14","title":"3RE-Net: Joint Loss-REcovery and Super-REsolution Neural Network for REal-Time Video","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Packet loss; Feature (linguistics); Artificial intelligence; Benchmark (surveying); Frame (networking); Real-time computing; Joint (building); Computer vision; Network packet; Telecommunications; Computer network","score_opus":0.02047339269934187,"score_gpt":0.26040401186756545,"score_spread":0.23993061916822359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389009069","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000052806914,0.00045527497,0.99494195,0.0011595148,0.0012823091,0.0007041453,0.000014293247,0.0010157306,0.00037399365],"genre_scores_gemma":[0.0020175963,0.00019897886,0.99515265,0.000907934,0.00077988417,0.00004620614,0.0000145039785,0.00008073632,0.00080148556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957019,0.000036854704,0.00061717577,0.0019550796,0.0006954292,0.0009935222],"domain_scores_gemma":[0.9971406,0.00079982844,0.00034614175,0.001208209,0.00032414624,0.00018107338],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011695882,0.00062027975,0.00066847773,0.0006202332,0.00047525592,0.0008025015,0.0021432491,0.00035588478,0.0000041377903],"category_scores_gemma":[0.0002347733,0.0005983198,0.00015083834,0.00068608683,0.0008745248,0.0013181972,0.0019333164,0.00061644806,0.000021520527],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034394256,0.00002484227,0.000036443227,0.00018654703,0.00002040427,0.00011083153,0.00032437462,0.06991926,0.002304375,0.012357645,0.001109608,0.9135713],"study_design_scores_gemma":[0.00014409529,0.00021847377,0.000073954216,0.00038574068,0.0000072042267,0.00006428321,3.3526938e-8,0.63460064,0.00049647223,0.36294344,0.00056467653,0.00050096837],"about_ca_topic_score_codex":0.000027257991,"about_ca_topic_score_gemma":0.000034688743,"teacher_disagreement_score":0.9130703,"about_ca_system_score_codex":0.00028317215,"about_ca_system_score_gemma":0.00034172484,"threshold_uncertainty_score":0.99964684},"labels":[],"label_agreement":null},{"id":"W4389543305","doi":"10.1109/embc40787.2023.10340196","title":"Single Image based Super Resolution Ultrasound Imaging Using Residual Learning of Wavelet Features","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer science; Residual; Wavelet; Computer vision; Image resolution; Iterative reconstruction; Artificial neural network; Deep learning; Wavelet transform; Pattern recognition (psychology); Algorithm","score_opus":0.020853356184570206,"score_gpt":0.2862550462669998,"score_spread":0.26540169008242964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389543305","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012669181,0.00009107195,0.98332065,0.0004706082,0.00006896687,0.00009645234,0.0000016665134,0.0017004651,0.0015809216],"genre_scores_gemma":[0.2875895,0.0000038537005,0.7119868,0.00009942483,0.00003185675,0.0000035029345,0.000007284455,0.000020020729,0.00025780578],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984325,0.000108494445,0.00026519407,0.00043103637,0.000363452,0.00039930662],"domain_scores_gemma":[0.9988987,0.00027232218,0.00013086663,0.00042697348,0.0002192035,0.000051977993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053414115,0.00016405035,0.00017721913,0.00033833846,0.00023685725,0.00020996027,0.0005523169,0.000049203973,0.000012632284],"category_scores_gemma":[0.00061439304,0.00015992371,0.00005561386,0.0010094031,0.0001439811,0.0012765927,0.00025546696,0.000231117,0.000008940199],"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":[0.0000067203055,0.000027294,0.00050631235,0.000035485235,0.0000035732864,0.000019578212,0.00023710533,0.0005201716,0.98993874,0.0010237009,0.0016137008,0.0060676183],"study_design_scores_gemma":[0.00019494051,0.0000439941,0.0011406374,0.000101801685,0.000006288086,0.000036169153,0.00011155952,0.46240965,0.5311023,0.0042281067,0.0003778037,0.0002467445],"about_ca_topic_score_codex":0.000060557235,"about_ca_topic_score_gemma":0.000002775551,"teacher_disagreement_score":0.46188948,"about_ca_system_score_codex":0.000084063075,"about_ca_system_score_gemma":0.00009277551,"threshold_uncertainty_score":0.65215},"labels":[],"label_agreement":null},{"id":"W4390189607","doi":"10.1109/iccvw60793.2023.00142","title":"MOFA: A Model Simplification Roadmap for Image Restoration on Mobile Devices","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Upsampling; Computer science; Image restoration; FLOPS; Image quality; Mobile device; Convolution (computer science); Image (mathematics); Code (set theory); Decoupling (probability); Software deployment; Artificial intelligence; Computer engineering; Algorithm; Computer vision; Parallel computing; Image processing; Engineering","score_opus":0.041636963238736814,"score_gpt":0.35633388891703,"score_spread":0.31469692567829316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390189607","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011579796,0.000013942898,0.9940456,0.00097464694,0.000041970157,0.0004285856,0.0000030106687,0.0022382452,0.0010959941],"genre_scores_gemma":[0.16688316,0.000007447125,0.8312688,0.00035084743,0.000025294607,0.0005308942,0.000015843878,0.000013025465,0.00090473297],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991119,0.0000137811585,0.00016416443,0.000362158,0.00016232564,0.00018569265],"domain_scores_gemma":[0.99916685,0.00008118638,0.000084980005,0.00046016515,0.00017047639,0.00003633385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024689044,0.00009852738,0.00008386355,0.00014350611,0.00015161508,0.00017727468,0.000489609,0.00004139677,0.00000120622],"category_scores_gemma":[0.000085512955,0.00008974561,0.000031373445,0.00044538078,0.00002108837,0.0012256791,0.00009436144,0.00005247089,0.00007075654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045720677,0.00021363718,0.000037571772,0.00019118557,0.0000134931215,0.0000046668465,0.0014700523,0.031046541,0.3394528,0.27335495,0.06988341,0.28428596],"study_design_scores_gemma":[0.00007452687,0.0000707895,0.000030162657,0.000010440947,0.0000013461195,5.722291e-7,0.000014761094,0.88275546,0.031280927,0.08434436,0.0013129078,0.000103757906],"about_ca_topic_score_codex":0.000002413818,"about_ca_topic_score_gemma":0.0000030003473,"teacher_disagreement_score":0.8517089,"about_ca_system_score_codex":0.00004995159,"about_ca_system_score_gemma":0.000050403367,"threshold_uncertainty_score":0.36597198},"labels":[],"label_agreement":null},{"id":"W4390992324","doi":"10.1109/bibm58861.2023.10385851","title":"Prostate MRI Super-Resolution using Discrete Residual Diffusion Model","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Nature","keywords":"Residual; Computer science; Artificial intelligence; Algorithm; Image quality; Magnetic resonance imaging; Encoder; Diffusion MRI; Computer vision; Pattern recognition (psychology); Image (mathematics); Radiology; Medicine","score_opus":0.03343343426205223,"score_gpt":0.314359778663332,"score_spread":0.2809263444012797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390992324","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01412679,0.000028827028,0.98083705,0.0016132878,0.000062436695,0.00017269263,0.0000026418,0.0025375474,0.0006187558],"genre_scores_gemma":[0.10484264,0.000032882173,0.89317787,0.00014196619,0.000026956997,0.000016235566,0.000006203425,0.000018090901,0.0017371278],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864334,0.000031657317,0.00019624761,0.000441596,0.00030988603,0.00037728297],"domain_scores_gemma":[0.99928385,0.000025968555,0.000055161436,0.00048172017,0.00008725261,0.00006602463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002468349,0.00013926708,0.000114609255,0.00017448628,0.0002620509,0.00018052795,0.0005598116,0.000050517898,0.000002427876],"category_scores_gemma":[0.000046954898,0.00011928005,0.00003249124,0.00068235496,0.000058647747,0.001423905,0.00079085806,0.00011909549,0.000029687157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048749,0.00011156155,0.00076428306,0.00013773814,0.000015743559,0.00008855948,0.0037705812,0.085592255,0.79442984,0.053079322,0.018088294,0.0438731],"study_design_scores_gemma":[0.00010181403,0.000022349266,0.00007871678,0.00003435197,0.0000020433245,0.0000064209407,0.000015778065,0.9344716,0.012510972,0.05242485,0.00016643973,0.00016467819],"about_ca_topic_score_codex":0.000030895015,"about_ca_topic_score_gemma":0.0000052220116,"teacher_disagreement_score":0.84887934,"about_ca_system_score_codex":0.00006530555,"about_ca_system_score_gemma":0.00007517388,"threshold_uncertainty_score":0.4864099},"labels":[],"label_agreement":null},{"id":"W4391157456","doi":"10.48550/arxiv.2401.11093","title":"Learned Image Compression with Dual-Branch Encoder and Conditional Information Coding","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Decoding methods; Entropy encoding; Codec; Encoder; Artificial intelligence; Conditional entropy; Data compression; Entropy (arrow of time); Arithmetic coding; Image compression; Pattern recognition (psychology); Algorithm; Context-adaptive binary arithmetic coding; Image processing; Image (mathematics); Principle of maximum entropy","score_opus":0.04004482594137631,"score_gpt":0.2092290314010227,"score_spread":0.16918420545964638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391157456","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015018109,0.0000731526,0.9811783,0.00023654096,0.000110739085,0.00022127687,0.000021875547,0.0008069116,0.00233309],"genre_scores_gemma":[0.89803517,0.000095365926,0.10142153,0.00011286908,0.000025389909,0.0000024562771,0.000049402784,0.000013269319,0.00024457427],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988062,0.000051516396,0.00016952542,0.00062617526,0.0001269803,0.00021962193],"domain_scores_gemma":[0.99891067,0.000068813155,0.00021395802,0.0004866243,0.00022442434,0.0000955239],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014682562,0.0002610389,0.000220208,0.00031022294,0.00020922463,0.00044898782,0.0005367758,0.0001600821,0.000010999103],"category_scores_gemma":[0.000024626572,0.0002572333,0.000048829243,0.00035717728,0.00020356136,0.002012667,0.0022165219,0.00067277986,0.000039267958],"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":[0.00023158819,0.00016554417,0.0008290562,0.0028086463,0.00026565077,0.001005626,0.003510858,0.04427368,0.00624929,0.92235345,0.0040411805,0.014265454],"study_design_scores_gemma":[0.0003069705,0.000042423173,0.0001812694,0.00047713716,0.00003382905,0.000036673024,0.000041106992,0.5977747,0.0014519334,0.398823,0.00045984195,0.00037110632],"about_ca_topic_score_codex":0.000016186243,"about_ca_topic_score_gemma":0.0000025049717,"teacher_disagreement_score":0.883017,"about_ca_system_score_codex":0.000114592585,"about_ca_system_score_gemma":0.00016440784,"threshold_uncertainty_score":0.99998796},"labels":[],"label_agreement":null},{"id":"W4392248437","doi":"10.1109/icce59016.2024.10444396","title":"A Dimmed Display Image Enhancement Technique for Energy Conservation","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer vision; Energy conservation; Computer graphics (images); Image (mathematics); Energy (signal processing); Artificial intelligence; Physics; Electrical engineering; Engineering","score_opus":0.01204540314749311,"score_gpt":0.29696206687684573,"score_spread":0.28491666372935265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392248437","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000065754207,0.00023678671,0.99262524,0.0026415046,0.00011672891,0.00030473384,0.0000020056966,0.0013825557,0.0026838994],"genre_scores_gemma":[0.02476221,0.000028178692,0.97041434,0.00091699656,0.000034360753,0.0012639901,0.00000716786,0.00001564796,0.0025570998],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905825,0.000015012314,0.00019046615,0.0003960846,0.00013262621,0.00020753758],"domain_scores_gemma":[0.9993712,0.00011748156,0.000036635935,0.00032898923,0.0001083215,0.0000373866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021370046,0.00012417244,0.00010058656,0.00010750877,0.00007657638,0.00028985756,0.00044889085,0.0000438682,0.00001079129],"category_scores_gemma":[0.00004815157,0.0001043345,0.00004925587,0.00031228742,0.000038892176,0.0010594436,0.00015861189,0.00005750776,0.0000071251666],"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":[0.000003354096,0.000024250108,0.0000010459767,0.00007562631,0.000006457304,0.000005326496,0.000060857168,2.256313e-7,0.61346084,0.3262191,0.01404357,0.04609935],"study_design_scores_gemma":[0.00004479384,0.000073796364,0.0000011558909,0.0000726715,0.0000027074593,0.000007896689,0.000002385442,0.17951262,0.6553525,0.12162433,0.043179594,0.0001255786],"about_ca_topic_score_codex":0.00001662508,"about_ca_topic_score_gemma":0.000005327351,"teacher_disagreement_score":0.2045948,"about_ca_system_score_codex":0.00006934826,"about_ca_system_score_gemma":0.000082344515,"threshold_uncertainty_score":0.4254637},"labels":[],"label_agreement":null},{"id":"W4392248678","doi":"10.1109/icce59016.2024.10444243","title":"Face Image Restoration Method Using Semantic and Transformer Splitting Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Transformer; Image restoration; Face (sociological concept); Artificial intelligence; Computer vision; Image (mathematics); Image processing; Engineering; Electrical engineering","score_opus":0.02119356792029839,"score_gpt":0.3441598668184133,"score_spread":0.3229662988981149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392248678","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025944458,0.0010503236,0.9962651,0.00047348114,0.000095868396,0.0001094298,1.7171698e-7,0.0010135962,0.00073258334],"genre_scores_gemma":[0.07961079,0.00002951667,0.9200727,0.00011033857,0.000038441038,0.000004120931,4.3820543e-7,0.000012222414,0.000121485544],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991535,0.000041831277,0.00016183876,0.00034679208,0.00011078538,0.00018526656],"domain_scores_gemma":[0.9996204,0.00009055317,0.000025604948,0.00018483872,0.000039958843,0.00003865223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045599727,0.00010679133,0.00009830585,0.000086923785,0.0001162279,0.00059160555,0.00019309953,0.00004421522,0.0000028075458],"category_scores_gemma":[0.000023922712,0.000092673545,0.000023802839,0.00037242443,0.000032353484,0.0019937833,0.00006199128,0.0001472645,0.0000022348058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000319755,0.00001348186,0.000029408619,0.00023855778,0.000016186152,0.000046156678,0.0014286148,0.0010958096,0.22515239,0.03246137,0.00026133488,0.73925346],"study_design_scores_gemma":[0.000028632172,0.000013040197,0.000009421925,0.00009638679,0.000006749443,0.00005596734,0.000022270737,0.9699196,0.016269712,0.01313456,0.00032427305,0.00011938131],"about_ca_topic_score_codex":0.000015031517,"about_ca_topic_score_gemma":0.0000022506579,"teacher_disagreement_score":0.9688238,"about_ca_system_score_codex":0.000032936765,"about_ca_system_score_gemma":0.00003272514,"threshold_uncertainty_score":0.5704866},"labels":[],"label_agreement":null},{"id":"W4392876840","doi":"10.1016/j.engappai.2024.108222","title":"CMISR: Circular medical image super-resolution","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Advanced Image Processing 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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Image (mathematics); Resolution (logic); Computer vision; Artificial intelligence; Computer graphics (images)","score_opus":0.014590990066875762,"score_gpt":0.29678810626383584,"score_spread":0.2821971161969601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392876840","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013526378,0.0015421598,0.99583435,0.0008012588,0.000119126045,0.00016850878,0.0000028465608,0.0012519156,0.00014456874],"genre_scores_gemma":[0.36480173,0.00006138153,0.6348178,0.00001949879,0.0000930282,0.00016515156,0.0000033016283,0.000017466573,0.00002062769],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986917,0.000010693397,0.00037082762,0.0003702568,0.00034989777,0.00020667192],"domain_scores_gemma":[0.9990714,0.00013299698,0.000033921853,0.00054505514,0.00012574399,0.0000908736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003511787,0.00012530287,0.00012232047,0.00021927996,0.0000613404,0.00013226268,0.0009662659,0.0000782839,0.000026574078],"category_scores_gemma":[0.00017588137,0.00013399699,0.000063335705,0.0009709087,0.000095377574,0.00047204946,0.00016580841,0.00022538046,0.00010438793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.5000054e-7,0.00005095554,9.638254e-7,0.00014971347,0.000010590361,0.000008278077,0.00018364613,0.0012904716,0.10311359,0.62302214,0.00010457967,0.27206433],"study_design_scores_gemma":[0.0000027725712,0.000011759009,0.000001933844,0.00007742161,0.000003427344,0.000015665662,0.00000840419,0.7531194,0.21360363,0.02951214,0.0035307312,0.00011273861],"about_ca_topic_score_codex":0.000012166969,"about_ca_topic_score_gemma":6.805209e-7,"teacher_disagreement_score":0.7518289,"about_ca_system_score_codex":0.000052018033,"about_ca_system_score_gemma":0.00010264327,"threshold_uncertainty_score":0.54642385},"labels":[],"label_agreement":null},{"id":"W4392904601","doi":"10.1109/icassp48485.2024.10448393","title":"Self-Supervised Face Image Restoration with a One-Shot Reference","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial intelligence; Semantics (computer science); Face (sociological concept); Ambiguity; Computer vision; Image (mathematics); Code (set theory); Image restoration; Pattern recognition (psychology); Image processing","score_opus":0.051647582597676545,"score_gpt":0.31290526326948165,"score_spread":0.2612576806718051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392904601","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003884169,0.00022275899,0.9731856,0.001460528,0.000038836537,0.00015714312,8.810167e-7,0.0046546534,0.019891195],"genre_scores_gemma":[0.11793217,0.000025442278,0.88115555,0.0001945093,0.000019306668,0.000032255175,0.0000024688552,0.00001353175,0.000624739],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989057,0.000028536277,0.00013619217,0.00047087503,0.00026137754,0.00019730284],"domain_scores_gemma":[0.99923855,0.0000405972,0.000027393866,0.00050972245,0.0001270295,0.00005669373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014748449,0.00013337398,0.000100589605,0.00010563473,0.00008342005,0.0006634077,0.0005971335,0.000042605086,0.000015181321],"category_scores_gemma":[0.000019318086,0.00010341122,0.000016084894,0.0005945825,0.000039206723,0.0026320035,0.00015891284,0.00017306476,0.00008900701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004468092,0.00032522235,0.00007420648,0.0008710546,0.00007443732,0.0003071885,0.006006868,0.000019307743,0.32136008,0.44038397,0.008531779,0.2220012],"study_design_scores_gemma":[0.00019276004,0.00034542903,0.000072175186,0.00026025905,0.000014026274,0.0000615015,0.000056199387,0.86955404,0.08743051,0.026727442,0.014749038,0.00053660804],"about_ca_topic_score_codex":0.0000095482665,"about_ca_topic_score_gemma":0.000007988802,"teacher_disagreement_score":0.86953473,"about_ca_system_score_codex":0.00006961935,"about_ca_system_score_gemma":0.00014968772,"threshold_uncertainty_score":0.63972557},"labels":[],"label_agreement":null},{"id":"W4392905879","doi":"10.32920/25413826","title":"Super-resolution of Audio Files Using Feed-forward Neural Networks for Music Storage and Transfer","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Encoder; Audio signal; Transfer (computing); SIGNAL (programming language); Audio signal flow; Speech recognition; Lossy compression; Digital audio; Computer hardware; Real-time computing; Speech coding; Artificial intelligence; Operating system","score_opus":0.03627816833476384,"score_gpt":0.28957994232812034,"score_spread":0.2533017739933565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392905879","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011549285,0.002660414,0.9835814,0.00025399178,0.000565147,0.0005848168,0.000027807362,0.00069053425,0.00008658876],"genre_scores_gemma":[0.43070602,0.000036317077,0.56890905,0.000077234254,0.00010378002,0.000053217867,0.000010012444,0.00002960395,0.00007474405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823827,0.000041330495,0.00039999082,0.00078486773,0.00020319356,0.00033232424],"domain_scores_gemma":[0.9990121,0.000111278016,0.00007692388,0.00055356376,0.00017716581,0.00006893093],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028883934,0.0003225078,0.00042346484,0.00021186001,0.00010126438,0.00026711854,0.00069933204,0.00025977328,0.0000057602715],"category_scores_gemma":[0.00003774454,0.00029645392,0.00016411133,0.00021795061,0.00013830067,0.000360663,0.0013763346,0.00049777055,2.7563212e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018457031,0.00029343573,0.0001100635,0.011430412,0.0004063773,0.00006928401,0.006174285,0.33821017,0.04620608,0.05371677,0.009362311,0.53383625],"study_design_scores_gemma":[0.0001065284,0.000056959114,0.000022071825,0.00032670665,0.00004644339,0.00001583479,0.000013025403,0.96343,0.00219225,0.033380825,0.00011229981,0.00029704635],"about_ca_topic_score_codex":0.00005108372,"about_ca_topic_score_gemma":0.000016261441,"teacher_disagreement_score":0.6252198,"about_ca_system_score_codex":0.00008375476,"about_ca_system_score_gemma":0.00009641946,"threshold_uncertainty_score":0.99994874},"labels":[],"label_agreement":null},{"id":"W4392905907","doi":"10.32920/25413826.v1","title":"Super-resolution of Audio Files Using Feed-forward Neural Networks for Music Storage and Transfer","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Encoder; Audio signal; Transfer (computing); SIGNAL (programming language); Speech recognition; Audio signal flow; Lossy compression; Matching (statistics); Digital audio; Computer hardware; Real-time computing; Speech coding; Artificial intelligence","score_opus":0.03627816833476384,"score_gpt":0.28957994232812034,"score_spread":0.2533017739933565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392905907","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011549285,0.002660414,0.9835814,0.00025399178,0.000565147,0.0005848168,0.000027807362,0.00069053425,0.00008658876],"genre_scores_gemma":[0.43070602,0.000036317077,0.56890905,0.000077234254,0.00010378002,0.000053217867,0.000010012444,0.00002960395,0.00007474405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823827,0.000041330495,0.00039999082,0.00078486773,0.00020319356,0.00033232424],"domain_scores_gemma":[0.9990121,0.000111278016,0.00007692388,0.00055356376,0.00017716581,0.00006893093],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028883934,0.0003225078,0.00042346484,0.00021186001,0.00010126438,0.00026711854,0.00069933204,0.00025977328,0.0000057602715],"category_scores_gemma":[0.00003774454,0.00029645392,0.00016411133,0.00021795061,0.00013830067,0.000360663,0.0013763346,0.00049777055,2.7563212e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018457031,0.00029343573,0.0001100635,0.011430412,0.0004063773,0.00006928401,0.006174285,0.33821017,0.04620608,0.05371677,0.009362311,0.53383625],"study_design_scores_gemma":[0.0001065284,0.000056959114,0.000022071825,0.00032670665,0.00004644339,0.00001583479,0.000013025403,0.96343,0.00219225,0.033380825,0.00011229981,0.00029704635],"about_ca_topic_score_codex":0.00005108372,"about_ca_topic_score_gemma":0.000016261441,"teacher_disagreement_score":0.6252198,"about_ca_system_score_codex":0.00008375476,"about_ca_system_score_gemma":0.00009641946,"threshold_uncertainty_score":0.99994874},"labels":[],"label_agreement":null},{"id":"W4393040678","doi":"10.3390/ai5010021","title":"Single Image Super Resolution Using Deep Residual Learning","year":2024,"lang":"en","type":"article","venue":"AI","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Toronto Zoo","funders":"","keywords":"Residual; Artificial intelligence; Deep learning; Computer science; Computer vision; Image (mathematics); Pattern recognition (psychology); Algorithm","score_opus":0.020915392498795867,"score_gpt":0.304840264488246,"score_spread":0.28392487198945016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393040678","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024486475,0.0012371225,0.9923428,0.00092246203,0.00016084021,0.000048093207,2.576395e-7,0.0015994307,0.0012403338],"genre_scores_gemma":[0.2064633,0.000007846575,0.7929442,0.00019487579,0.00010063444,0.0000037345324,0.0000013316663,0.00001776966,0.0002663015],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905765,0.000049896196,0.00013156273,0.00034366563,0.0001770716,0.00024016673],"domain_scores_gemma":[0.99957323,0.000048893457,0.000023263105,0.0002457455,0.00007253921,0.00003632137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021465243,0.000097749005,0.000078484074,0.000120333665,0.00016238226,0.00051044737,0.00033361962,0.000045438534,0.000009478228],"category_scores_gemma":[0.00011400639,0.00009724284,0.00002986681,0.00038676284,0.00005766594,0.0018521046,0.0002509097,0.00025962328,0.00004811183],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047230296,0.000039296036,0.00008273923,0.00008628574,0.000011738957,0.00017953753,0.0016183248,0.0003657851,0.837184,0.012874241,0.0024983175,0.14505498],"study_design_scores_gemma":[0.000050601335,0.00006657878,0.000028967152,0.00015270208,0.000006699119,0.00008158879,0.0000228482,0.88889676,0.068601444,0.025798524,0.016085094,0.00020818788],"about_ca_topic_score_codex":0.000013922469,"about_ca_topic_score_gemma":0.0000019094746,"teacher_disagreement_score":0.88853097,"about_ca_system_score_codex":0.00010136324,"about_ca_system_score_gemma":0.000056714318,"threshold_uncertainty_score":0.49222556},"labels":[],"label_agreement":null},{"id":"W4393156072","doi":"10.1609/aaai.v38i2.27846","title":"Spherical Pseudo-Cylindrical Representation for Omnidirectional Image Super-resolution","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Shandong Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China; University of Alberta; National Science Foundation","keywords":"Omnidirectional antenna; Representation (politics); Resolution (logic); Image (mathematics); Computer vision; Computer science; Artificial intelligence; Physics; Telecommunications","score_opus":0.08439333663475698,"score_gpt":0.3585141589910858,"score_spread":0.27412082235632884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393156072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029509496,0.000057154648,0.9856378,0.0064148144,0.0005796417,0.0004737925,0.0000063498355,0.0005333922,0.003346092],"genre_scores_gemma":[0.5951223,0.000027539645,0.40417522,0.00010643565,0.00014633188,0.00012326578,0.0000012051116,0.000018069552,0.00027967917],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99797815,0.000016848073,0.0005054609,0.00068638276,0.00048793937,0.00032521095],"domain_scores_gemma":[0.99849933,0.00021314091,0.00016349752,0.0002472914,0.0008052764,0.000071440096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004348151,0.00020943015,0.00021322782,0.00013078024,0.0002322821,0.00055634876,0.0012694816,0.00010086585,0.00003112128],"category_scores_gemma":[0.0008266867,0.0001642163,0.00017001697,0.0009359228,0.00030504033,0.0011710603,0.00029359173,0.00030912543,0.00004419927],"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":[0.000053257383,0.000092375354,0.000015169604,0.00007323433,0.00001042499,6.3640476e-7,0.00037014383,0.000017319631,0.2496538,0.6595323,0.0011121139,0.08906924],"study_design_scores_gemma":[0.000010032302,0.00009279451,0.000017662403,0.00012439725,0.000007043203,0.000008772831,0.00005350707,0.3451046,0.354576,0.29963517,0.00026064014,0.0001094073],"about_ca_topic_score_codex":0.000014691965,"about_ca_topic_score_gemma":0.0000017940603,"teacher_disagreement_score":0.5921713,"about_ca_system_score_codex":0.00010782939,"about_ca_system_score_gemma":0.00014822015,"threshold_uncertainty_score":0.66965467},"labels":[],"label_agreement":null},{"id":"W4394627375","doi":"10.1109/jiot.2024.3386572","title":"Small Insulator Defects Detection Based on Multiscale Feature Interaction Transformer for UAV-Assisted Power IoVT","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of British Columbia","funders":"National Natural Science Foundation of China-Guangdong Joint Fund","keywords":"Computer science; Object detection; Computation; Artificial intelligence; Real-time computing; Computer vision; Pattern recognition (psychology); Algorithm","score_opus":0.020599581576644994,"score_gpt":0.2856219741744582,"score_spread":0.26502239259781324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394627375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033158097,0.0002094355,0.96286845,0.0005292816,0.0024078446,0.00020692898,0.0000022977817,0.0003608886,0.00025676677],"genre_scores_gemma":[0.74267745,0.0000058972096,0.2566676,0.00031513267,0.00008252081,0.000017177035,7.358937e-7,0.000026743817,0.00020670754],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99867356,0.00005314956,0.00037005582,0.0003548709,0.00029075812,0.00025757813],"domain_scores_gemma":[0.9990034,0.00019950359,0.00023716924,0.0002143265,0.00025112776,0.00009448263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044670046,0.00021840812,0.00021857723,0.00043149915,0.00009115398,0.00049297884,0.0005964215,0.0001462311,0.00000869572],"category_scores_gemma":[0.00012829679,0.0001827521,0.0002768524,0.00025695981,0.000040918603,0.0015345828,0.000022760061,0.0007689172,0.0000056443746],"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":[0.00041930893,0.00021065082,0.000028264745,0.00029795,0.00009186005,0.00006033645,0.002717789,0.00023040501,0.638318,0.00019896775,0.002290185,0.35513628],"study_design_scores_gemma":[0.00038394285,0.000668232,0.00007120599,0.001117575,0.000021410204,0.00035068323,0.000017875896,0.42274383,0.56807476,0.0019877774,0.004377579,0.00018514426],"about_ca_topic_score_codex":0.000008720545,"about_ca_topic_score_gemma":0.000004817982,"teacher_disagreement_score":0.7095194,"about_ca_system_score_codex":0.00023789746,"about_ca_system_score_gemma":0.00007589484,"threshold_uncertainty_score":0.7452414},"labels":[],"label_agreement":null},{"id":"W4395097710","doi":"10.1109/jetcas.2024.3392868","title":"Enhancing Image Quality by Reducing Compression Artifacts Using Dynamic Window Swin Transformer","year":2024,"lang":"en","type":"article","venue":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Image compression; Compression artifact; Data compression; Pixel; Image quality; Transformer; Image processing; Engineering; Image (mathematics)","score_opus":0.02602552822990455,"score_gpt":0.33096843423982614,"score_spread":0.3049429060099216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395097710","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3576151,0.0039832587,0.637131,0.00018582644,0.00069016375,0.00010505094,0.0000016729083,0.00013886497,0.00014903377],"genre_scores_gemma":[0.9923985,0.00057965703,0.006725633,0.00004010835,0.00014323658,0.0000037686239,0.000001125182,0.000016219392,0.00009175456],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981235,0.00020024099,0.00059827644,0.00041377495,0.0003046501,0.00035950742],"domain_scores_gemma":[0.9993465,0.00011705976,0.00014517202,0.00015786172,0.00011388235,0.00011955131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008723297,0.00020277433,0.00031608428,0.0002804866,0.00032507413,0.00087795296,0.00020228693,0.00010010237,9.180131e-7],"category_scores_gemma":[0.00006029819,0.00017213087,0.000035335095,0.0004625817,0.000036310183,0.0008972238,0.000014216517,0.0006802877,3.8717687e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036110034,0.000027017139,0.000077939796,0.0003141304,0.000018615467,0.00005626354,0.00233823,0.00025915308,0.8935487,0.0003361998,0.000039068273,0.102981016],"study_design_scores_gemma":[0.0006372199,0.00020491358,0.00086649455,0.006668624,0.000024394854,0.0011879154,0.00033065144,0.92999613,0.05472261,0.002851066,0.0017322726,0.0007777209],"about_ca_topic_score_codex":0.00006942222,"about_ca_topic_score_gemma":0.000006607713,"teacher_disagreement_score":0.929737,"about_ca_system_score_codex":0.00014743715,"about_ca_system_score_gemma":0.000083339255,"threshold_uncertainty_score":0.84661204},"labels":[],"label_agreement":null},{"id":"W4396494564","doi":"10.18280/ts.410249","title":"Image Super-Resolution Reconstruction in Sports Scenarios and Its Application in Motion Analysis","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer vision; Computer science; Motion (physics); Artificial intelligence; Resolution (logic); Motion analysis; Image (mathematics); Geology","score_opus":0.009739958985871908,"score_gpt":0.25569988168849467,"score_spread":0.24595992270262276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396494564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15744175,0.00029627062,0.841359,0.00035356317,0.00003196119,0.00022697807,0.0000014835375,0.00022794852,0.000060998038],"genre_scores_gemma":[0.88142115,0.000043329044,0.11840007,0.000023796387,0.000021693153,0.00006879876,0.000009229731,0.000005650681,0.000006297544],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885976,0.0000415214,0.00029409037,0.00044338364,0.00019479977,0.00016641623],"domain_scores_gemma":[0.99972314,0.000021103362,0.000049470626,0.00013066396,0.0000430922,0.00003254876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045694632,0.000108113054,0.00013033625,0.00069101714,0.000040963438,0.00012740592,0.000145202,0.000048149814,0.000013127257],"category_scores_gemma":[0.00000985955,0.000113686816,0.000032853877,0.0013496856,0.000026507949,0.0014175155,0.000051643285,0.00012643232,0.0000031543643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016666922,0.00013396349,0.015139581,0.00015034695,0.000026248199,0.000044215,0.0011420522,0.0015491551,0.11616443,0.005324377,0.00002119269,0.8602878],"study_design_scores_gemma":[0.00012302565,0.000019234418,0.022741228,0.00007139096,0.000018654091,0.000015226773,0.000017010645,0.9692527,0.0024879607,0.0050903317,0.000046010347,0.00011720935],"about_ca_topic_score_codex":0.00003286548,"about_ca_topic_score_gemma":0.000058814046,"teacher_disagreement_score":0.9677036,"about_ca_system_score_codex":0.00015241584,"about_ca_system_score_gemma":0.000026963618,"threshold_uncertainty_score":0.46360138},"labels":[],"label_agreement":null},{"id":"W4398784900","doi":"10.1016/j.knosys.2024.111992","title":"Progressive reconstruction-decoupled face super-resolution framework with controllable knowledge guidance","year":2024,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Face (sociological concept); Computer science; Computer vision; Artificial intelligence; Sociology","score_opus":0.013932949816374256,"score_gpt":0.2868639537857171,"score_spread":0.27293100396934283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398784900","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00086414727,0.07639077,0.91197896,0.00033295635,0.0024310062,0.0010918892,0.000013091413,0.0034606357,0.0034365458],"genre_scores_gemma":[0.6966963,0.000017313272,0.30020127,0.000028735994,0.00048869563,0.0009122862,0.0000073303922,0.000082388564,0.0015657156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99658394,0.00030676584,0.00068808405,0.0012687488,0.00039763044,0.00075486075],"domain_scores_gemma":[0.996708,0.00064825226,0.00023945785,0.0012050021,0.0009758512,0.0002234553],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00083972095,0.00052517775,0.0006072577,0.0004394916,0.000431643,0.0012790924,0.001210406,0.00028558125,0.00001624306],"category_scores_gemma":[0.00029032133,0.00043131204,0.00013620376,0.001980119,0.0003049936,0.0014217165,0.00016463299,0.0005791593,0.0003067041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011375865,0.0032917655,0.005321811,0.023542808,0.0016172588,0.0017115287,0.019238984,0.012912592,0.06707767,0.35322022,0.06896594,0.44196182],"study_design_scores_gemma":[0.00084098213,0.00031342736,0.000023883773,0.00720394,0.000040082956,0.00028831372,0.000117230156,0.9523052,0.005192653,0.0036204713,0.029314488,0.00073934544],"about_ca_topic_score_codex":0.000018721552,"about_ca_topic_score_gemma":0.000019494293,"teacher_disagreement_score":0.93939257,"about_ca_system_score_codex":0.00054268347,"about_ca_system_score_gemma":0.0012046815,"threshold_uncertainty_score":0.99981385},"labels":[],"label_agreement":null},{"id":"W4399070826","doi":"10.1016/j.cviu.2024.104034","title":"DHBSR: A deep hybrid representation-based network for blind image super resolution","year":2024,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Toronto","funders":"","keywords":"Artificial intelligence; Representation (politics); Image (mathematics); Computer science; Computer vision; Resolution (logic); Pattern recognition (psychology)","score_opus":0.046929933836349566,"score_gpt":0.3382567293562341,"score_spread":0.2913267955198845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399070826","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001218632,0.00092773954,0.9947127,0.0019416971,0.00055803574,0.00042477896,0.0000049950563,0.0010708343,0.00023737007],"genre_scores_gemma":[0.08471113,0.000044266377,0.914341,0.00049496396,0.00028231298,0.000029439962,0.000021977252,0.000033651995,0.00004129182],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809086,0.00007364765,0.00033438057,0.00081904617,0.00025481448,0.0004272608],"domain_scores_gemma":[0.99876076,0.00048947055,0.00007439407,0.00042961264,0.00012439265,0.00012135564],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004921318,0.00024447058,0.00022807276,0.00027991546,0.0004874176,0.0019417257,0.00040994977,0.000057224297,0.000008424191],"category_scores_gemma":[0.0000539126,0.00022543933,0.00011367459,0.00051725644,0.00015875137,0.002110359,0.00029103903,0.00019173294,0.000008162326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005681795,0.00031057515,0.00011053363,0.0017123612,0.000179842,0.0006628583,0.002583088,0.0021934572,0.049285278,0.28182715,0.24315664,0.41741005],"study_design_scores_gemma":[0.0005349008,0.00016635803,0.000012916628,0.000313197,0.00001250022,0.000041332056,0.000028847411,0.88320625,0.0015318206,0.111935206,0.001965162,0.0002514999],"about_ca_topic_score_codex":0.00000254213,"about_ca_topic_score_gemma":0.0000013216614,"teacher_disagreement_score":0.8810128,"about_ca_system_score_codex":0.00021550375,"about_ca_system_score_gemma":0.00007392611,"threshold_uncertainty_score":0.99909437},"labels":[],"label_agreement":null},{"id":"W4399141302","doi":"10.3847/1538-4357/ad3e76","title":"Autoencoding Labeled Interpolator, Inferring Parameters from Image and Image from Parameters","year":2024,"lang":"en","type":"article","venue":"The Astrophysical Journal","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image (mathematics); Artificial intelligence; Computer science; Computer vision","score_opus":0.013159514167658232,"score_gpt":0.2692793139298648,"score_spread":0.25611979976220656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399141302","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30634958,0.0003376309,0.6918023,0.000795977,0.00025170442,0.00005658966,0.000007225183,0.0003769516,0.000022059028],"genre_scores_gemma":[0.40614125,0.00003102621,0.59351426,0.00014655711,0.00013568343,0.000004928624,9.5210885e-7,0.000018867506,0.0000065003724],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833745,0.00014481581,0.0003301336,0.0004479827,0.00034156264,0.00039804447],"domain_scores_gemma":[0.99862415,0.0005560252,0.0001334109,0.0004499499,0.000061753926,0.0001747249],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00021798632,0.00025672896,0.0002744884,0.00009733948,0.0002765318,0.0024319333,0.0011488979,0.00004441356,0.000007788445],"category_scores_gemma":[0.00012013261,0.00017670284,0.00013238234,0.00027241214,0.00028364913,0.0021517675,0.0006432415,0.0010072726,0.000041125866],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028672248,0.000040225146,0.000056686815,0.000010424881,0.00013244389,0.00026746612,0.0015361473,0.00002527362,0.82770616,0.0021873142,0.00043218397,0.16757701],"study_design_scores_gemma":[0.00039611905,0.00019398863,0.0013279862,0.00064476434,0.00008094779,0.00014237723,0.000112919835,0.6252936,0.074903816,0.29623154,0.00019836238,0.00047358012],"about_ca_topic_score_codex":0.000072482064,"about_ca_topic_score_gemma":7.9163664e-7,"teacher_disagreement_score":0.7528023,"about_ca_system_score_codex":0.0000997129,"about_ca_system_score_gemma":0.00006741918,"threshold_uncertainty_score":0.99860364},"labels":[],"label_agreement":null},{"id":"W4399383124","doi":"10.3390/jimaging10060137","title":"PlantSR: Super-Resolution Improves Object Detection in Plant Images","year":2024,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"National Key Research and Development Program of China; Shandong Agricultural University","keywords":"Artificial intelligence; Computer science; Object detection; Object (grammar); Computer vision; Resolution (logic); Pattern recognition (psychology); Image resolution","score_opus":0.008062067264422471,"score_gpt":0.2665062766449798,"score_spread":0.2584442093805573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399383124","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077157156,0.0042207525,0.98619187,0.00086354394,0.0005842924,0.000048529684,0.0000015739772,0.0002018841,0.00017182449],"genre_scores_gemma":[0.79452354,0.0001239057,0.20511314,0.000076315795,0.00013159539,0.000002131158,2.8447715e-7,0.0000108991,0.000018204726],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989304,0.000045635075,0.00037390087,0.00018697427,0.00024402041,0.00021908309],"domain_scores_gemma":[0.99950314,0.00008972436,0.00013328689,0.00014136621,0.00008765754,0.000044816403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006106288,0.00011304291,0.00015244508,0.00056055747,0.000057242887,0.0003963335,0.00041592252,0.000025136462,0.000001307913],"category_scores_gemma":[0.00011512589,0.000094909345,0.00006600565,0.0003788633,0.000034588375,0.002880321,0.00010199564,0.00039180001,0.000003697867],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000205339,0.00002611633,0.000249662,0.00006534945,0.000009208575,0.00082205643,0.00047506485,0.000026415484,0.5763686,0.00016288666,0.0006557763,0.42111838],"study_design_scores_gemma":[0.00033215553,0.00015598409,0.001731628,0.0011365067,0.000014772437,0.0063408874,0.00012921794,0.56515473,0.4028639,0.018780077,0.0030563902,0.00030380304],"about_ca_topic_score_codex":0.00001890069,"about_ca_topic_score_gemma":0.000005001815,"teacher_disagreement_score":0.7868078,"about_ca_system_score_codex":0.00019354351,"about_ca_system_score_gemma":0.00012455577,"threshold_uncertainty_score":0.38702908},"labels":[],"label_agreement":null},{"id":"W4400573491","doi":"10.1145/3641519.3657507","title":"Deep Hybrid Camera Deblurring for Smartphone Cameras","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Social Innovation","funders":"Universitas Brawijaya","keywords":"Deblurring; Computer science; Computer vision; Artificial intelligence; Computer graphics (images); Image processing; Image (mathematics); Image restoration","score_opus":0.012288734286618095,"score_gpt":0.28536325574060123,"score_spread":0.27307452145398314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400573491","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025201897,0.0014972179,0.9919003,0.0010996655,0.00035784947,0.00017116264,9.998263e-7,0.002739188,0.0019816002],"genre_scores_gemma":[0.11091377,0.000019849082,0.887227,0.0006551917,0.00008272434,0.000092630886,0.000001590435,0.000021482865,0.0009857758],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989261,0.000010226915,0.00016944624,0.00045664565,0.00013605486,0.00030151557],"domain_scores_gemma":[0.999326,0.0001301559,0.000024204626,0.0003763528,0.000077268174,0.00006602986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016056858,0.00013673754,0.00012301936,0.00012169673,0.000110273126,0.00042885524,0.0006115398,0.000023306617,0.000014925068],"category_scores_gemma":[0.000062329425,0.00011990193,0.00006570042,0.00027913484,0.00003679672,0.0010026307,0.00020974156,0.00011036103,0.000051265175],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033291153,0.000023818835,0.000022538321,0.000110775654,0.000019524388,0.000051824365,0.00028824442,0.000040080635,0.010690786,0.052478705,0.009482774,0.9267876],"study_design_scores_gemma":[0.000074630625,0.00004384581,0.0000079502115,0.0000622666,0.000004600816,0.00006088243,0.000007573516,0.83271813,0.08065334,0.05412614,0.03202113,0.00021948927],"about_ca_topic_score_codex":0.00001493724,"about_ca_topic_score_gemma":0.0000044130816,"teacher_disagreement_score":0.9265681,"about_ca_system_score_codex":0.00006024622,"about_ca_system_score_gemma":0.00006978596,"threshold_uncertainty_score":0.48894587},"labels":[],"label_agreement":null},{"id":"W4401372757","doi":"10.3389/frsen.2024.1417417","title":"ARISGAN: Extreme super-resolution of arctic surface imagery using generative adversarial networks","year":2024,"lang":"en","type":"article","venue":"Frontiers in Remote Sensing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"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":"Office of Naval Research; Natural Environment Research Council; UK Research and Innovation; U.S. Department of Defense","keywords":"Computer science; Satellite imagery; Remote sensing; Artificial intelligence; Geology","score_opus":0.024923547521378774,"score_gpt":0.2675834671640255,"score_spread":0.24265991964264672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401372757","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040956237,0.0041744215,0.988268,0.00019624317,0.002599562,0.00018005156,0.0000010443549,0.00036944813,0.00011560003],"genre_scores_gemma":[0.20152086,0.0000668211,0.7981486,0.000050150866,0.00014894402,4.1929038e-8,0.0000023730609,0.0000266028,0.00003561238],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980673,0.00017409291,0.00043466338,0.0006065888,0.0002826022,0.00043476216],"domain_scores_gemma":[0.9991269,0.000087127475,0.00012226214,0.0004547374,0.00015073322,0.000058238],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00051386387,0.00023325822,0.00034437535,0.00030164616,0.00011522151,0.00020058925,0.00033138273,0.0001397996,8.0795377e-7],"category_scores_gemma":[0.00013537503,0.0002506165,0.00009389213,0.0010137643,0.00018159072,0.0010865409,0.00023589957,0.00042664268,8.163528e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075951415,0.000036653517,0.00023523653,0.00028568617,0.00009159605,0.0005550792,0.0028321547,0.19570594,0.13431574,0.00041594432,0.0025395649,0.66291046],"study_design_scores_gemma":[0.00013478192,0.00002078241,0.000018642244,0.00062631,0.000015737902,0.000057552592,0.00006491762,0.97829115,0.008942566,0.011415691,0.00016994948,0.00024192213],"about_ca_topic_score_codex":0.00024331117,"about_ca_topic_score_gemma":0.000009885748,"teacher_disagreement_score":0.7825852,"about_ca_system_score_codex":0.0004441206,"about_ca_system_score_gemma":0.00017416626,"threshold_uncertainty_score":0.99999464},"labels":[],"label_agreement":null},{"id":"W4402104563","doi":"10.1016/j.engappai.2024.109227","title":"Combining optical flow and Swin Transformer for Space-Time video super-resolution","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Computer science; Optical flow; Transformer; Computer vision; Voltage; Electrical engineering; Image (mathematics)","score_opus":0.01492096999736599,"score_gpt":0.2786102088261962,"score_spread":0.2636892388288302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402104563","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026213142,0.00062975625,0.99726236,0.00075697125,0.00005825096,0.00036172342,0.0000068345944,0.0006001834,0.000061806764],"genre_scores_gemma":[0.24005772,0.000023227616,0.7595797,0.0000073528317,0.00003975835,0.00025312652,0.0000033429867,0.000014453041,0.000021266926],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907976,0.0000045654047,0.0002827668,0.00032539197,0.00011436586,0.00019314112],"domain_scores_gemma":[0.9993143,0.0002555521,0.000022552767,0.000262875,0.00008792395,0.000056773297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024260517,0.00011960899,0.00013218001,0.00016471824,0.00007719682,0.00012996678,0.00033331724,0.00005612343,0.0000030423694],"category_scores_gemma":[0.00007845076,0.0001282567,0.00004664787,0.00045881863,0.00007250839,0.00040565335,0.000042964526,0.000119591816,0.000014105887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032906091,0.00002807914,3.6407695e-7,0.00012685933,0.000009053272,4.1995338e-7,0.000330211,0.0067726034,0.18200429,0.41056836,0.000035341473,0.40012112],"study_design_scores_gemma":[0.0000061719566,0.000033042546,0.0000013469835,0.000058194895,0.000006068546,0.0000046133327,0.000008762454,0.7043023,0.26211417,0.03159085,0.0017773429,0.00009714325],"about_ca_topic_score_codex":0.0000030380327,"about_ca_topic_score_gemma":4.8463863e-7,"teacher_disagreement_score":0.69752973,"about_ca_system_score_codex":0.000030638024,"about_ca_system_score_gemma":0.000035871584,"threshold_uncertainty_score":0.5230157},"labels":[],"label_agreement":null},{"id":"W4402261023","doi":"10.1109/igarss53475.2024.10641390","title":"Loss Functions Analysis of Performance Improvements in Single-Image Super-Resolution","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Image (mathematics); Computer vision; Artificial intelligence","score_opus":0.0157043534662252,"score_gpt":0.2755171299058316,"score_spread":0.25981277643960643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402261023","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02920774,0.00014987767,0.96737313,0.0001992928,0.000089478424,0.00007080107,0.0000028011596,0.00043975082,0.0024671364],"genre_scores_gemma":[0.71277994,0.000012826266,0.28668728,0.000034115852,0.000006700975,0.000013544346,0.000004053714,0.0000046702926,0.0004568881],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990495,0.000015997723,0.00025227247,0.00031898654,0.00018214477,0.00018113389],"domain_scores_gemma":[0.99947375,0.000032832522,0.00003444537,0.0003488305,0.00008421676,0.00002589825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000195653,0.00009181074,0.00014462566,0.000720423,0.000040092844,0.00010916855,0.00035752833,0.00003317581,0.000025988396],"category_scores_gemma":[0.000023047003,0.00008281212,0.00006546483,0.0028893321,0.000056782294,0.0015692272,0.00017343387,0.00009892972,0.000012458503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016072125,0.0005161708,0.009428548,0.00036508878,0.0003515486,0.000033522992,0.0012124812,0.001298549,0.51958644,0.013568117,0.0006509569,0.4529725],"study_design_scores_gemma":[0.00006010483,0.00007696251,0.0024553887,0.000051650764,0.000046485187,0.0000018107256,0.000014870448,0.96168524,0.033848267,0.0008994304,0.0007453512,0.00011444608],"about_ca_topic_score_codex":0.000036295543,"about_ca_topic_score_gemma":0.00003319282,"teacher_disagreement_score":0.9603867,"about_ca_system_score_codex":0.00010179161,"about_ca_system_score_gemma":0.000040957726,"threshold_uncertainty_score":0.33769804},"labels":[],"label_agreement":null},{"id":"W4402290942","doi":"10.1016/j.imavis.2024.105254","title":"Diff-STAR: Exploring student-teacher adaptive reconstruction through diffusion-based generation for image harmonization","year":2024,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Harmonization; Star (game theory); Diffusion; Image (mathematics); Computer science; Computer vision; Artificial intelligence; Physics; Astrophysics","score_opus":0.05639306339122327,"score_gpt":0.35672722552421515,"score_spread":0.3003341621329919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402290942","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052386094,0.00061754993,0.9445881,0.00044562653,0.0005766862,0.00035077785,0.000002758079,0.0009568852,0.00007549431],"genre_scores_gemma":[0.33553356,0.00004242733,0.66398764,0.00008848993,0.00026205482,0.000030306288,0.0000101522,0.000023681141,0.000021690059],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983799,0.00007118228,0.00034016598,0.0007154134,0.00023116947,0.00026218526],"domain_scores_gemma":[0.9991475,0.00018335978,0.00011953758,0.00026404904,0.00023258457,0.000053015017],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00036755996,0.00021621595,0.00018237927,0.00015795758,0.00049162644,0.0011014068,0.0002605535,0.00005899567,0.000004495148],"category_scores_gemma":[0.00008737304,0.0002031414,0.00006631199,0.00036063185,0.00007234253,0.0030733591,0.00023940418,0.0001806566,0.0000045409743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010898851,0.00006486531,0.00007139511,0.000111996604,0.0000135128985,0.0000150332735,0.0018754564,0.00007503732,0.23718141,0.0014223437,0.00041619386,0.75874186],"study_design_scores_gemma":[0.00028662468,0.00012770515,0.00013494804,0.00033789765,0.000012661173,0.000018527311,0.000108975015,0.9601258,0.035160076,0.0030750188,0.00037366638,0.00023806801],"about_ca_topic_score_codex":0.000008013784,"about_ca_topic_score_gemma":9.546931e-7,"teacher_disagreement_score":0.9600508,"about_ca_system_score_codex":0.000089472735,"about_ca_system_score_gemma":0.00004524951,"threshold_uncertainty_score":0.99993557},"labels":[],"label_agreement":null},{"id":"W4402295586","doi":"10.1016/j.image.2024.117187","title":"DJUHNet: A deep representation learning-based scheme for the task of joint image upsampling and hashing","year":2024,"lang":"en","type":"article","venue":"Signal Processing Image Communication","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; McMaster University; University of Toronto","funders":"","keywords":"Upsampling; Hash function; Joint (building); Computer science; Scheme (mathematics); Artificial intelligence; Task (project management); Representation (politics); Image (mathematics); Feature hashing; Feature learning; Deep learning; Computer vision; Pattern recognition (psychology); Theoretical computer science; Hash table; Mathematics; Double hashing; Computer security","score_opus":0.037780303363326384,"score_gpt":0.3343431171695501,"score_spread":0.29656281380622374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402295586","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000212205,0.010861901,0.9853281,0.0024977115,0.00002362272,0.00036876157,0.0000015627525,0.00059890555,0.00010723693],"genre_scores_gemma":[0.45331696,0.0000690237,0.546407,0.00005574713,0.00001680829,0.000088168985,0.000010687153,0.000020209423,0.000015393225],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858063,0.0001317549,0.00041464713,0.00040625702,0.000248296,0.00021843173],"domain_scores_gemma":[0.9977495,0.0007828575,0.00031096858,0.0006489655,0.00046899923,0.000038681817],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011648153,0.0001726816,0.00018512535,0.00016785708,0.0006540338,0.0010922793,0.000907164,0.000058915524,0.0000027471874],"category_scores_gemma":[0.0004644033,0.0001442757,0.00006937472,0.00054535107,0.00022842913,0.002132017,0.0003688855,0.00040104755,0.0000018341691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003215301,0.0000676595,0.000038793944,0.0011437341,0.000027815704,0.0000021640685,0.003950337,0.0027249528,0.56547695,0.0018529223,0.0001362674,0.42454624],"study_design_scores_gemma":[0.00016189588,0.000051336927,0.000041541956,0.0005916077,0.000021964017,0.000008512975,0.00016820194,0.93886554,0.04301951,0.016489286,0.00042809773,0.00015248884],"about_ca_topic_score_codex":0.000015640853,"about_ca_topic_score_gemma":0.0000020215832,"teacher_disagreement_score":0.9361406,"about_ca_system_score_codex":0.000056029796,"about_ca_system_score_gemma":0.00015098166,"threshold_uncertainty_score":0.9999447},"labels":[],"label_agreement":null},{"id":"W4402307167","doi":"10.18280/ts.410432","title":"Enhanced Image Super Resolution Using ResNet Generative Adversarial Networks","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Majmaah University","keywords":"Adversarial system; Generative grammar; Generative adversarial network; Artificial intelligence; Superresolution; Computer science; Image (mathematics); Computer vision; Resolution (logic); Pattern recognition (psychology)","score_opus":0.019773963683638793,"score_gpt":0.2840179612381556,"score_spread":0.2642439975545168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402307167","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019274552,0.0006405991,0.9949175,0.00032503542,0.00045579718,0.00025358042,0.000004687304,0.0009206851,0.0005546507],"genre_scores_gemma":[0.416201,0.000018290017,0.5829847,0.00019071493,0.00048807875,0.000028236585,0.000009020906,0.000019020557,0.00006092856],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982483,0.00010466395,0.0003151286,0.0005923864,0.00034040812,0.0003991439],"domain_scores_gemma":[0.9994077,0.00006598169,0.00006048961,0.0002752033,0.000113271155,0.000077359335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039571486,0.00021663102,0.00016716766,0.0001441387,0.00021406867,0.0004991554,0.00050669437,0.0000768078,0.00008148981],"category_scores_gemma":[0.000018843053,0.00020561722,0.000078704965,0.0004581116,0.000099783174,0.0017439034,0.00021352121,0.00024174759,0.000013950852],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063906045,0.00008845954,0.00000551282,0.000056620243,0.00006498985,0.00010917141,0.001663975,0.009814265,0.9248033,0.01888955,0.005035768,0.03940449],"study_design_scores_gemma":[0.00021983666,0.000094309646,0.000013298504,0.00011189948,0.000015294154,0.000012567011,0.0000138816895,0.93268913,0.06075813,0.0045315186,0.0013008963,0.00023921201],"about_ca_topic_score_codex":0.000013308862,"about_ca_topic_score_gemma":0.000002898862,"teacher_disagreement_score":0.92287487,"about_ca_system_score_codex":0.00020765507,"about_ca_system_score_gemma":0.00012419836,"threshold_uncertainty_score":0.83848274},"labels":[],"label_agreement":null},{"id":"W4402473647","doi":"10.1109/ccece59415.2024.10667314","title":"Video Upscaling in Extreme Edge Environments","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Computer graphics (images); Computer vision","score_opus":0.04491688997898841,"score_gpt":0.27972972037736726,"score_spread":0.23481283039837886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402473647","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030663479,0.0008689802,0.99220794,0.000563254,0.0001151769,0.000054831416,1.4442075e-7,0.0006971741,0.0051858416],"genre_scores_gemma":[0.23579709,0.000032621105,0.76236624,0.00023394066,0.000021753358,0.00001469816,3.1759126e-7,0.000008806584,0.0015245574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992172,0.000014071939,0.00013501054,0.0003294125,0.00012817908,0.00017612049],"domain_scores_gemma":[0.9996434,0.00003928061,0.000011337522,0.00027141618,0.0000033241972,0.00003126015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015521533,0.000080597216,0.0000672549,0.000118009884,0.000024432518,0.00016892156,0.00045756224,0.000029066998,0.000023198856],"category_scores_gemma":[0.000019330915,0.00007156958,0.000020883755,0.00027917532,0.000023421593,0.0009382079,0.00025700798,0.00011424216,0.000119775206],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001889717,0.000076344,0.0007133806,0.00006631678,0.000006790356,0.00023079771,0.000827534,0.000045335088,0.10734987,0.07369813,0.0026696266,0.814314],"study_design_scores_gemma":[0.00011005049,0.00002794414,0.00044928645,0.00021600588,0.0000019659817,0.00002519945,0.000012990414,0.6909324,0.07682789,0.165216,0.06584867,0.0003316237],"about_ca_topic_score_codex":0.00000519256,"about_ca_topic_score_gemma":0.0000016773621,"teacher_disagreement_score":0.81398237,"about_ca_system_score_codex":0.00006683689,"about_ca_system_score_gemma":0.000019798861,"threshold_uncertainty_score":0.2918523},"labels":[],"label_agreement":null},{"id":"W4402715917","doi":"10.1109/cvpr52733.2024.02358","title":"Frequency-Aware Event-Based Video Deblurring for Real-World Motion Blur","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Deblurring; Motion blur; Computer science; Computer vision; Artificial intelligence; Event (particle physics); Motion (physics); Image restoration; Computer graphics (images); Image (mathematics); Image processing; Physics","score_opus":0.021093380851254066,"score_gpt":0.32613022906343914,"score_spread":0.3050368482121851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402715917","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000075085816,0.00017397858,0.9924368,0.0016245046,0.0002907608,0.00027126473,0.0000025681659,0.0035026218,0.0016223897],"genre_scores_gemma":[0.29915056,0.0000048077795,0.6995205,0.0002973734,0.00006195975,0.000120421035,0.000004796689,0.000020360514,0.0008192041],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874127,0.000022546505,0.00023549795,0.0005267668,0.0001854328,0.00028850624],"domain_scores_gemma":[0.99917185,0.00014439643,0.000045407807,0.00045103865,0.00012288033,0.00006442788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027433046,0.00015673168,0.00012518173,0.00024659725,0.00012496262,0.00036549612,0.00059917854,0.000044267555,0.000017658585],"category_scores_gemma":[0.000055508062,0.00014080147,0.000086764085,0.00059505884,0.000028255814,0.0011820046,0.000103477934,0.00011868812,0.000024588382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008953266,0.000121816905,0.0005852617,0.0009605796,0.000033639517,0.00006799504,0.00026683407,0.0009846076,0.048146814,0.37120298,0.011306228,0.5663143],"study_design_scores_gemma":[0.00008799102,0.00003733178,0.000045811437,0.00020590745,0.0000058837304,0.000003292552,0.0000035138844,0.81359917,0.07548076,0.10872775,0.0016095588,0.00019305298],"about_ca_topic_score_codex":0.000061611325,"about_ca_topic_score_gemma":0.000074599135,"teacher_disagreement_score":0.81261456,"about_ca_system_score_codex":0.0001518107,"about_ca_system_score_gemma":0.000141276,"threshold_uncertainty_score":0.5741717},"labels":[],"label_agreement":null},{"id":"W4402727316","doi":"10.1109/cvpr52733.2024.00013","title":"FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion","keywords":"Deblurring; Joint (building); Computer science; Feature (linguistics); Artificial intelligence; Computer vision; Net (polyhedron); Flow (mathematics); Iterative method; Pattern recognition (psychology); Image (mathematics); Algorithm; Image restoration; Image processing; Mathematics; Engineering","score_opus":0.0213175504869657,"score_gpt":0.2916277711215007,"score_spread":0.27031022063453497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402727316","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052542975,0.0011488337,0.9909511,0.001569638,0.000070402966,0.00036141096,0.00000443383,0.00059495546,0.000044878867],"genre_scores_gemma":[0.1289414,0.000035062185,0.8702874,0.00010608377,0.000015047696,0.00009950145,0.0000072382263,0.000015460955,0.0004927655],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989818,0.000017626431,0.00015415999,0.0005213399,0.00011627116,0.00020883224],"domain_scores_gemma":[0.9996021,0.00003395703,0.00003433135,0.00018950393,0.000090155336,0.000049989503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016934426,0.00016993842,0.00013324183,0.00010445347,0.00016960465,0.00050740875,0.000117307536,0.000045254917,0.0000017250578],"category_scores_gemma":[0.000023761982,0.00012969028,0.000023552207,0.00015719622,0.00004500026,0.0010508104,0.00019909587,0.000111937654,6.3006286e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047037996,0.00010506091,0.00030309823,0.0014829156,0.00012109524,0.00005933256,0.003494631,0.00043089327,0.56591105,0.0074318596,0.0019874955,0.41862553],"study_design_scores_gemma":[0.00030990443,0.00013503197,0.0008069637,0.0004950801,0.000012902955,0.00007890337,0.000032974043,0.989148,0.007054599,0.0012568376,0.00047607432,0.00019273153],"about_ca_topic_score_codex":0.000009979235,"about_ca_topic_score_gemma":0.00004280393,"teacher_disagreement_score":0.9887171,"about_ca_system_score_codex":0.00007552494,"about_ca_system_score_gemma":0.000024611192,"threshold_uncertainty_score":0.5288616},"labels":[],"label_agreement":null},{"id":"W4402727731","doi":"10.1109/cvpr52733.2024.01985","title":"PAPR in Motion: Seamless Point-level 3D Scene Interpolation","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpolation (computer graphics); Computer science; Computer vision; Motion (physics); Computer graphics (images); Point (geometry); Artificial intelligence; Mathematics; Geometry","score_opus":0.02930357247357336,"score_gpt":0.30509922192035294,"score_spread":0.2757956494467796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402727731","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017098586,0.00024292023,0.9909724,0.0018549595,0.00020391034,0.000083744235,7.6552124e-7,0.0012293987,0.005240926],"genre_scores_gemma":[0.3428276,0.000007713226,0.6564114,0.0002020983,0.000027288736,0.000010976533,0.0000012546412,0.000007103151,0.0005045478],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991468,0.000025392983,0.00017969239,0.0003410193,0.00014278184,0.00016431743],"domain_scores_gemma":[0.9995751,0.00003977375,0.000022408827,0.00028131253,0.000049063146,0.000032367996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023158132,0.00009570169,0.00008337109,0.00019967726,0.000032188585,0.00028544356,0.000418797,0.00004157321,0.000032521653],"category_scores_gemma":[0.000035875288,0.00008566914,0.00002344284,0.000575195,0.000024251489,0.0017292756,0.00022612208,0.00014449905,0.000056819328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002661414,0.000043612956,0.00035329614,0.00008357534,0.000004168395,0.00004875656,0.00065847114,0.00001717856,0.009094915,0.07303527,0.001455711,0.9152024],"study_design_scores_gemma":[0.00006159221,0.000019608287,0.00060023554,0.00017452623,0.0000010152351,0.00002214908,0.000013559818,0.92072463,0.008874879,0.06855446,0.00081457186,0.00013876842],"about_ca_topic_score_codex":0.000018471583,"about_ca_topic_score_gemma":0.000014466763,"teacher_disagreement_score":0.92070746,"about_ca_system_score_codex":0.000078323166,"about_ca_system_score_gemma":0.000046075627,"threshold_uncertainty_score":0.34934863},"labels":[],"label_agreement":null},{"id":"W4402733583","doi":"10.1109/cvpr52733.2024.00879","title":"Arbitrary-Scale Image Generation and Upsampling Using Latent Diffusion Model and Implicit Neural Decoder","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Upsampling; Computer science; Scale (ratio); Image (mathematics); Artificial intelligence; Diffusion; Computer vision; Physics","score_opus":0.03538021516341322,"score_gpt":0.30861441186108257,"score_spread":0.27323419669766935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402733583","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25347406,0.00058117736,0.74505925,0.0002945199,0.000044121767,0.000075773554,7.929517e-7,0.0004126527,0.000057687463],"genre_scores_gemma":[0.27835006,0.000053621625,0.72130626,0.00021029222,0.000030478894,0.0000045634724,0.000001059808,0.000011096398,0.000032570322],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913275,0.000011187444,0.00014799328,0.000426511,0.00010605258,0.00017547868],"domain_scores_gemma":[0.9996817,0.000020325673,0.00002385333,0.00017457237,0.00003799388,0.00006152858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010006055,0.00012499819,0.000096970514,0.000090894326,0.00015597379,0.00063660037,0.0001309107,0.000041368552,0.0000012824544],"category_scores_gemma":[0.000006405584,0.000105195606,0.000018528883,0.0001295583,0.00003953808,0.0014964825,0.0003257913,0.00011182396,6.540865e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016878903,0.000013269421,0.00016013905,0.000054580025,0.0000035388862,0.000009273252,0.00040181688,0.0026287334,0.9539139,0.004821326,0.00005893659,0.037932772],"study_design_scores_gemma":[0.000050046794,0.000012782674,0.00007607219,0.00002942443,0.0000047939175,0.00006741128,0.0000041956678,0.9628196,0.0124004455,0.024396054,0.0000069648513,0.00013222473],"about_ca_topic_score_codex":0.000022169668,"about_ca_topic_score_gemma":0.0000092401615,"teacher_disagreement_score":0.96019083,"about_ca_system_score_codex":0.000027355385,"about_ca_system_score_gemma":0.000023137629,"threshold_uncertainty_score":0.61387515},"labels":[],"label_agreement":null},{"id":"W4402797646","doi":"10.1101/2024.09.07.611785","title":"niiv: Interactive Self-supervised Neural Implicit Isotropic Volume Reconstruction","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Isotropy; Volume (thermodynamics); Artificial intelligence; Computer science; Mathematics; Physics; Optics; Thermodynamics","score_opus":0.009287226774268936,"score_gpt":0.23329471724219406,"score_spread":0.22400749046792512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402797646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21399513,0.0023521613,0.7617547,0.0015249596,0.006471717,0.0013548725,0.000100436664,0.012383787,0.000062223306],"genre_scores_gemma":[0.5526868,0.00009179932,0.44625932,0.0002118841,0.00037809866,0.00024644446,1.2407227e-7,0.000116825,0.000008693371],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99571264,0.00017585052,0.00074975594,0.0021118254,0.0004734691,0.00077646156],"domain_scores_gemma":[0.99603117,0.000067052875,0.00052411645,0.0023070856,0.00079264794,0.00027791527],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00035393066,0.00084045803,0.0007052964,0.00063517626,0.0002342327,0.0013900154,0.00224501,0.00054673397,0.000022506807],"category_scores_gemma":[0.00017072682,0.0009188555,0.00025966403,0.001030408,0.00013672958,0.0012389112,0.0035674463,0.0020828818,0.00020746286],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004015261,0.0003052635,0.0023964895,0.0019140004,0.0005114977,0.00038648225,0.00018354996,0.000070530754,0.98556376,0.005409165,0.002008249,0.0012108635],"study_design_scores_gemma":[0.00068406103,0.00025200946,0.008190392,0.0022193072,0.0002723016,0.0000017628391,0.0000079588835,0.6872315,0.29230368,0.0016024656,0.003908739,0.0033258344],"about_ca_topic_score_codex":0.00003649304,"about_ca_topic_score_gemma":9.253062e-7,"teacher_disagreement_score":0.6932601,"about_ca_system_score_codex":0.0008277655,"about_ca_system_score_gemma":0.0007317364,"threshold_uncertainty_score":0.99964666},"labels":[],"label_agreement":null},{"id":"W4402816875","doi":"10.1109/cvpr52733.2024.02428","title":"TTA-EVF: Test-Time Adaptation for Event-based Video Frame Interpolation via Reliable Pixel and Sample Estimation","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Defense Acquisition Program Administration; National Research Foundation of Korea","keywords":"Frame (networking); Interpolation (computer graphics); Computer science; Sample (material); Adaptation (eye); Pixel; Event (particle physics); Estimation; Test (biology); Artificial intelligence; Algorithm; Computer vision; Telecommunications; Geology; Psychology; Engineering","score_opus":0.013614794249700551,"score_gpt":0.2898112622908872,"score_spread":0.2761964680411867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402816875","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011269857,0.000115263705,0.9963601,0.0015244263,0.00011449763,0.00034238186,0.000007157988,0.0013592392,0.00006420648],"genre_scores_gemma":[0.16986012,0.0000022078214,0.829465,0.00031395192,0.000026173488,0.000091625276,0.00001976098,0.00001546629,0.00020571312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902666,0.0000146368275,0.00023549331,0.0004015171,0.00015549846,0.00016618609],"domain_scores_gemma":[0.99865204,0.000854837,0.000072411276,0.0002422126,0.00013386752,0.000044626453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029597475,0.0001301169,0.00011041249,0.00016139267,0.00011416191,0.00042883898,0.00020764195,0.00006310653,0.000013501606],"category_scores_gemma":[0.00065237394,0.00012219345,0.000034330817,0.00029550723,0.000030512298,0.0017574854,0.00006661869,0.000084907246,0.000019663748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045440596,0.00012712258,0.00009615608,0.0006076707,0.000017223463,0.0000022405618,0.00093467697,0.006412918,0.19282714,0.02397953,0.006612506,0.76833737],"study_design_scores_gemma":[0.00009886153,0.00013306068,0.000022716276,0.00010750953,0.0000074959457,0.0000030756808,0.0000041012418,0.8554623,0.010560466,0.13247427,0.0009975851,0.00012851987],"about_ca_topic_score_codex":0.000031402607,"about_ca_topic_score_gemma":0.0000038412336,"teacher_disagreement_score":0.84904945,"about_ca_system_score_codex":0.000066094275,"about_ca_system_score_gemma":0.000084821615,"threshold_uncertainty_score":0.49829045},"labels":[],"label_agreement":null},{"id":"W4402861989","doi":"10.2139/ssrn.4967694","title":"Improving Attention Quality for Facial Image Super-Resolution Using Counterfactual Attention Learning","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Counterfactual thinking; Artificial intelligence; Quality (philosophy); Image quality; Image (mathematics); Computer science; Computer vision; Resolution (logic); Psychology; Cognitive psychology; Social psychology; Epistemology; Philosophy","score_opus":0.025749326715846496,"score_gpt":0.33671977086779936,"score_spread":0.31097044415195285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402861989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06515288,0.003089042,0.92886895,0.0003753202,0.0012334809,0.0005072135,0.000012737802,0.00073053886,0.00002984283],"genre_scores_gemma":[0.62567824,0.0006012111,0.37187204,0.000031738982,0.0010134092,0.00006350164,0.000052009378,0.000109883906,0.00057794567],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9945763,0.0002944162,0.0009275235,0.0010510857,0.000662073,0.002488646],"domain_scores_gemma":[0.99778146,0.00008175545,0.00095119927,0.0004599605,0.00062975567,0.00009586241],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004979735,0.00050709076,0.00048782985,0.0004439644,0.0007889882,0.0015541251,0.001216192,0.00038397414,0.00000238844],"category_scores_gemma":[0.00040911033,0.0005289197,0.0004897003,0.00025465238,0.000097772325,0.0013620589,0.00150782,0.0067990427,0.000011884945],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018868287,0.00018582382,0.00029508027,0.0013945806,0.00045725945,0.000015890932,0.0007532656,0.0018421902,0.53202915,0.06338765,0.00007651483,0.39937392],"study_design_scores_gemma":[0.00038979095,0.00023173516,0.000044329172,0.00041297122,0.00011153357,0.0002892213,0.00029470923,0.47315073,0.00093190407,0.5233742,0.00016346232,0.0006054544],"about_ca_topic_score_codex":0.0001588976,"about_ca_topic_score_gemma":0.000085827145,"teacher_disagreement_score":0.56052536,"about_ca_system_score_codex":0.0044632107,"about_ca_system_score_gemma":0.0037841147,"threshold_uncertainty_score":0.9997162},"labels":[],"label_agreement":null},{"id":"W4402916193","doi":"10.1109/cvprw63382.2024.00668","title":"Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Image (mathematics); Image resolution","score_opus":0.02468231201995942,"score_gpt":0.30246556957194026,"score_spread":0.27778325755198086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402916193","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000035968344,0.005000004,0.9720155,0.0023822985,0.00048595085,0.00013063248,0.000002514784,0.0029085625,0.017038547],"genre_scores_gemma":[0.100679755,0.0002275403,0.89206076,0.00021891562,0.00009236791,0.000045358294,0.0000065758686,0.000033400178,0.0066353274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833465,0.00009125983,0.00022986718,0.0006690334,0.0002863944,0.0003887791],"domain_scores_gemma":[0.9988809,0.00016411084,0.000023771221,0.00067807187,0.00015663568,0.00009649543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005591455,0.00019953682,0.0001642568,0.00017919161,0.00013128607,0.00068195444,0.0010152863,0.0000764235,0.00015072388],"category_scores_gemma":[0.00014090537,0.00017155353,0.00007136716,0.0007663678,0.000101846046,0.0019091273,0.0005542422,0.00026083688,0.00046081713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018900806,0.00036432105,0.00023722596,0.0006155743,0.00010106608,0.000726755,0.002365334,0.00005670588,0.012762867,0.26616415,0.19021954,0.52636755],"study_design_scores_gemma":[0.00008518952,0.000073961965,0.0003385343,0.000089943605,0.00000482812,0.00004624193,0.000015307805,0.94220656,0.0021612663,0.034144573,0.020456105,0.00037751073],"about_ca_topic_score_codex":0.00008276691,"about_ca_topic_score_gemma":0.00010539919,"teacher_disagreement_score":0.9421498,"about_ca_system_score_codex":0.000077915574,"about_ca_system_score_gemma":0.00010759975,"threshold_uncertainty_score":0.699575},"labels":[],"label_agreement":null},{"id":"W4402945832","doi":"10.1016/j.cviu.2024.104182","title":"Efficient degradation representation learning network for remote sensing image super-resolution","year":2024,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Science Foundation of Shandong Province","keywords":"Computer science; Representation (politics); Artificial intelligence; Degradation (telecommunications); Image (mathematics); Computer vision; Remote sensing; Superresolution; Resolution (logic); Pattern recognition (psychology); Geology; Telecommunications","score_opus":0.041953533719019366,"score_gpt":0.3283758128595982,"score_spread":0.28642227914057883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402945832","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00056261924,0.0004901465,0.9956024,0.0010766081,0.0005612092,0.0003329591,9.748701e-7,0.0012021529,0.0001709205],"genre_scores_gemma":[0.08127935,0.000046321813,0.9182338,0.000104574334,0.00022431911,0.0000013506291,0.00001589896,0.00002827862,0.00006612133],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982646,0.00010636795,0.00030703683,0.000710346,0.00024293388,0.00036871838],"domain_scores_gemma":[0.9990961,0.00035434568,0.000090238595,0.00025676974,0.000119421384,0.00008311805],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00063266413,0.0002090707,0.00018961955,0.00024837046,0.00065468973,0.0016446825,0.0001965826,0.000068080895,0.0000016152352],"category_scores_gemma":[0.00007963717,0.00019944803,0.00008090232,0.0005614093,0.00009549356,0.0010346244,0.00028717174,0.00022839864,0.0000046016794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005557732,0.000023418259,0.0000066663906,0.00035944802,0.000033853084,0.00007418583,0.0015481478,0.0060197986,0.071142666,0.036926225,0.007905316,0.8759047],"study_design_scores_gemma":[0.00022548535,0.00012891745,0.000011812425,0.00049562234,0.000011709753,0.00006986818,0.00007906995,0.9521347,0.0011275264,0.044682097,0.0008110571,0.00022212593],"about_ca_topic_score_codex":0.0000064527494,"about_ca_topic_score_gemma":0.0000010284573,"teacher_disagreement_score":0.9461149,"about_ca_system_score_codex":0.00027559485,"about_ca_system_score_gemma":0.000037857284,"threshold_uncertainty_score":0.99939173},"labels":[],"label_agreement":null},{"id":"W4403021820","doi":"10.1109/tip.2024.3468023","title":"Event-Assisted Blurriness Representation Learning for Blurry Image Unfolding","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Computer vision; Image (mathematics); Representation (politics); Event (particle physics); Pattern recognition (psychology); Image processing; Physics","score_opus":0.024622398211017915,"score_gpt":0.34092332440220346,"score_spread":0.31630092619118555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403021820","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004658626,0.0005108046,0.99318963,0.0007993483,0.00059453485,0.00043839967,0.0000055312676,0.0035856324,0.00041023514],"genre_scores_gemma":[0.47400472,0.000023494758,0.5248859,0.00007234878,0.00007154694,0.00027591363,0.0000038374687,0.00006257196,0.0005996538],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974629,0.00008160395,0.0005180373,0.0010073,0.00040396326,0.000526181],"domain_scores_gemma":[0.9986027,0.00027214974,0.00016660643,0.0004321498,0.00041117947,0.000115252056],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00043573885,0.0003559346,0.00028621693,0.0005525395,0.0008959552,0.0019369408,0.00064296165,0.000114155446,0.000014970403],"category_scores_gemma":[0.00007440942,0.00036607095,0.0002034958,0.0014985703,0.00013290056,0.0052246624,0.000011649592,0.00047809898,0.000031203366],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028609364,0.000104068415,0.0000016045084,0.00062915107,0.0000258172,0.000037679387,0.00071717944,0.0011359118,0.31141213,0.00013647896,0.00012729717,0.6856441],"study_design_scores_gemma":[0.00028066558,0.00008147,0.000007021829,0.0006835366,0.00004471549,0.00008891387,0.00010806815,0.6276737,0.36642075,0.003477502,0.0007538953,0.0003798075],"about_ca_topic_score_codex":0.000007780852,"about_ca_topic_score_gemma":0.0000023557448,"teacher_disagreement_score":0.6852642,"about_ca_system_score_codex":0.00020376184,"about_ca_system_score_gemma":0.00025373866,"threshold_uncertainty_score":0.9998791},"labels":[],"label_agreement":null},{"id":"W4403330974","doi":"10.1016/j.jvcir.2024.104302","title":"OODNet: A deep blind JPEG image compression deblocking network using out-of-distribution detection","year":2024,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Concordia University","funders":"","keywords":"Artificial intelligence; Computer vision; Deblocking filter; Computer science; Image compression; JPEG; Image (mathematics); Compression (physics); JPEG 2000; Distribution (mathematics); Pattern recognition (psychology); Mathematics; Image processing; Materials science","score_opus":0.044823066995531104,"score_gpt":0.40936598965021814,"score_spread":0.364542922654687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403330974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022641774,0.0036320512,0.9729095,0.0002442249,0.00023657753,0.00014177395,0.0000010851959,0.00010166467,0.000091361675],"genre_scores_gemma":[0.6238743,0.00064644514,0.3753583,0.000016247473,0.00007912503,0.000002589254,0.00000784404,0.000010584499,0.000004555271],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982832,0.00034737348,0.0006832878,0.00019791158,0.00033547697,0.00015272888],"domain_scores_gemma":[0.9979641,0.00027174078,0.00069945777,0.00036900863,0.0006296473,0.000066080494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088547,0.0001345629,0.00022711685,0.00019913621,0.00024435308,0.00045207806,0.00044016517,0.000071787494,0.0000034919528],"category_scores_gemma":[0.00024877876,0.0001255991,0.00009061512,0.0005693594,0.00012885565,0.0030754071,0.00029867742,0.00036002437,0.000001261634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010398745,0.0001260805,0.00015320686,0.00013280226,0.000049086873,0.000011242381,0.0013746225,0.00036188873,0.76708376,0.0005232276,0.00019265253,0.22988746],"study_design_scores_gemma":[0.00040944412,0.00017277063,0.0005143348,0.00066411466,0.000047884518,0.00015188812,0.00018655289,0.879653,0.10968479,0.007956886,0.0004102784,0.00014805411],"about_ca_topic_score_codex":0.000013403298,"about_ca_topic_score_gemma":0.0000033179147,"teacher_disagreement_score":0.8792911,"about_ca_system_score_codex":0.00010165923,"about_ca_system_score_gemma":0.000066577806,"threshold_uncertainty_score":0.51217824},"labels":[],"label_agreement":null},{"id":"W4403381611","doi":"10.1007/978-3-031-68106-6_3","title":"Advancements in Deep Learning-Based Super-resolution for Remote Sensing: A Comprehensive Review and Future Directions","year":2024,"lang":"en","type":"review","venue":"Unsupervised and semi-supervised learning","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Remote sensing; Deep learning; Computer science; Data science; Artificial intelligence; Geology","score_opus":0.030821433868682226,"score_gpt":0.3271346738528481,"score_spread":0.29631323998416587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403381611","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000021859432,0.7745793,0.22188875,0.0006192759,0.00022962144,0.0017443597,0.000010553224,0.0008707169,0.00005527387],"genre_scores_gemma":[0.0000041839494,0.82750744,0.17118394,0.00046574214,0.00021153742,0.000098706754,0.00018956364,0.00015225682,0.00018663635],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947851,0.000757796,0.0011899369,0.0019560494,0.00040359137,0.0009075276],"domain_scores_gemma":[0.99773145,0.0005876131,0.00035183842,0.00069677905,0.0003576649,0.00027464438],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000713939,0.0011493125,0.0025138357,0.00091292843,0.0006849601,0.00047766903,0.0006697885,0.00054226816,0.000010188187],"category_scores_gemma":[0.00035293525,0.0010425271,0.00044159064,0.0017566413,0.00016478637,0.0007379151,0.00053056475,0.0022363611,0.000012128861],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000074082586,0.000022816474,0.0000018521752,0.05634995,0.000064724154,0.000030367712,0.0003786918,0.00002824353,0.00001228473,0.000051385072,0.00007773995,0.9429745],"study_design_scores_gemma":[0.000466451,0.00017250555,9.866861e-7,0.031684276,0.00065354625,0.00010383366,0.00007722088,0.22096168,0.0000014150867,0.00027937646,0.74479204,0.000806664],"about_ca_topic_score_codex":0.000026180767,"about_ca_topic_score_gemma":0.0000106266825,"teacher_disagreement_score":0.9421679,"about_ca_system_score_codex":0.00027055733,"about_ca_system_score_gemma":0.00028561152,"threshold_uncertainty_score":0.9992025},"labels":[],"label_agreement":null},{"id":"W4403759712","doi":"10.1007/978-3-031-72664-4_25","title":"Rejection Sampling IMLE: Designing Priors for Better Few-Shot Image Synthesis","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Prior probability; Shot (pellet); One shot; Image (mathematics); Sampling (signal processing); Artificial intelligence; Computer vision; Computer graphics (images); Algorithm; Bayesian probability","score_opus":0.034253625639089263,"score_gpt":0.3043514396817086,"score_spread":0.27009781404261934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403759712","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009286185,0.00062833214,0.99361324,0.0011608189,0.0015223102,0.0007215922,0.000008407717,0.0011381775,0.001197845],"genre_scores_gemma":[0.004405647,0.00003209426,0.9936019,0.00087977696,0.00057963456,0.0001041752,0.0000032923458,0.00010204107,0.00029144817],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99523914,0.00002584369,0.00066951104,0.0023665756,0.0008101287,0.00088877766],"domain_scores_gemma":[0.9964016,0.0013341936,0.00036282907,0.0013518033,0.00040632728,0.00014323113],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013609688,0.0007219382,0.00062831014,0.0012906526,0.00048620318,0.0017077674,0.0032065355,0.0003822757,0.000008073498],"category_scores_gemma":[0.00046117508,0.00068757107,0.0002225617,0.0008205103,0.00064079516,0.0016140976,0.00142056,0.0010163905,0.000028788274],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010614755,0.000018768675,0.0000060358925,0.00032016542,0.000020662901,0.00005967889,0.00061246194,0.0015246286,0.019556465,0.0063703544,0.000071948314,0.9714282],"study_design_scores_gemma":[0.00009145331,0.00012223366,0.0000050881017,0.0017103961,0.000028147999,0.000102606165,2.8013824e-7,0.37727937,0.066367224,0.5515212,0.001861847,0.00091017573],"about_ca_topic_score_codex":0.000007183172,"about_ca_topic_score_gemma":0.0000087837725,"teacher_disagreement_score":0.97051805,"about_ca_system_score_codex":0.0006171627,"about_ca_system_score_gemma":0.00046678784,"threshold_uncertainty_score":0.99955755},"labels":[],"label_agreement":null},{"id":"W4403944836","doi":"10.1049/ipr2.13230","title":"Simultaneous single image super‐resolution and blind Gaussian denoising via slim ghost full‐frequency residual blocks","year":2024,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Residual; Noise reduction; Gaussian; Superresolution; Artificial intelligence; Resolution (logic); Computer science; Image denoising; Pattern recognition (psychology); Image (mathematics); Algorithm; Computer vision; Physics","score_opus":0.012941766502646149,"score_gpt":0.27827548351569165,"score_spread":0.2653337170130455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403944836","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070358072,0.011696306,0.9742515,0.0016701128,0.0002487931,0.00034776411,0.000010155984,0.0029214094,0.0018181219],"genre_scores_gemma":[0.4596285,0.00004788834,0.5397685,0.00012913534,0.00021184582,0.000022529228,0.0000084295525,0.00007010278,0.000113067676],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99601203,0.000109284316,0.00070206734,0.0015100192,0.0006648673,0.0010017479],"domain_scores_gemma":[0.9981181,0.00027177876,0.00020948856,0.0007041689,0.0004323238,0.00026412483],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00063614,0.00059290224,0.00042998002,0.00052492216,0.000938487,0.004265045,0.0010287529,0.00022966295,0.000016932685],"category_scores_gemma":[0.0004903549,0.000587474,0.000089601774,0.001296885,0.00054255925,0.0062461453,0.0006311456,0.0007447533,0.00004046762],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030739608,0.00010225749,0.000020870926,0.0005909759,0.000017182638,0.0008817176,0.0018389731,0.00001626194,0.74751264,0.00019615138,0.00018407078,0.24860819],"study_design_scores_gemma":[0.00082941144,0.0004984355,0.000035598412,0.0026069127,0.00011718109,0.0030759692,0.00028396733,0.7200894,0.21504217,0.054297376,0.0012724288,0.0018511749],"about_ca_topic_score_codex":0.000031260544,"about_ca_topic_score_gemma":0.000016245875,"teacher_disagreement_score":0.7200731,"about_ca_system_score_codex":0.00027650018,"about_ca_system_score_gemma":0.0003097397,"threshold_uncertainty_score":0.9996577},"labels":[],"label_agreement":null},{"id":"W4404037135","doi":"10.1109/mlsp58920.2024.10734781","title":"DisCrossFormer: A Deep Light-Weight Image Super Resolution Network Using Disentangled Visual Signal Processing and Correlated Cross-Attention Transformer Operation","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Toronto","funders":"","keywords":"Transformer; Computer science; Artificial intelligence; Signal processing; Computer vision; High resolution; Digital signal processing; Electrical engineering; Engineering; Voltage; Computer hardware; Remote sensing; Geology","score_opus":0.009606829241975309,"score_gpt":0.3043622400811835,"score_spread":0.2947554108392082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404037135","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022084486,0.002244441,0.9729551,0.0003963319,0.00021287859,0.00036706033,0.0000022850543,0.0012850557,0.00045233616],"genre_scores_gemma":[0.63253427,0.000048493283,0.36688718,0.000081694765,0.00015092033,0.000029921766,0.000022202905,0.000036318517,0.00020899084],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978468,0.0000574918,0.00047962627,0.0007430266,0.00034966285,0.0005233895],"domain_scores_gemma":[0.99943453,0.000031864576,0.000071791,0.00017496254,0.00017307868,0.000113746464],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003640997,0.0002988232,0.00021677195,0.00016248431,0.0007638793,0.0026952836,0.00026598026,0.00014304116,0.000031967087],"category_scores_gemma":[0.000010271963,0.00024631657,0.000080994745,0.0007777673,0.00017386246,0.0075347675,0.00010859797,0.0002825731,0.000013333216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010870493,0.00017082194,0.0018602498,0.00045121793,0.000054902503,0.00008621565,0.0021313203,0.000734506,0.77664226,0.0054260157,0.00029945502,0.21203433],"study_design_scores_gemma":[0.00028886547,0.00008188139,0.00035721518,0.0003232986,0.0000325337,0.00011786952,0.000029760211,0.97618836,0.017753828,0.0040651863,0.00039925583,0.00036196478],"about_ca_topic_score_codex":0.000011574606,"about_ca_topic_score_gemma":0.000015874173,"teacher_disagreement_score":0.97545385,"about_ca_system_score_codex":0.00017081379,"about_ca_system_score_gemma":0.00012652413,"threshold_uncertainty_score":0.9999989},"labels":[],"label_agreement":null},{"id":"W4404349826","doi":"10.1186/s13640-024-00657-w","title":"Learned scalable video coding for humans and machines","year":2024,"lang":"en","type":"article","venue":"EURASIP Journal on Image and Video Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Codec; Analytics; Video tracking; Coding (social sciences); Artificial intelligence; Scalability; Videoconferencing; Video processing; Scalable Video Coding; Data compression; Computer vision; Multimedia; Motion compensation; Computer hardware; Data mining; Database","score_opus":0.028799698775308902,"score_gpt":0.33822064429046617,"score_spread":0.30942094551515725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404349826","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030563897,0.010355679,0.9806148,0.0043838653,0.00018175991,0.00014012733,0.0000018196544,0.00049443066,0.0007711747],"genre_scores_gemma":[0.34591508,0.00074904395,0.6514262,0.00092824025,0.00036140814,0.00002097329,0.0000010099203,0.00005479438,0.0005432674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840695,0.00004866679,0.00036107944,0.00054349727,0.0002456183,0.00039421234],"domain_scores_gemma":[0.99909276,0.000229832,0.00015419147,0.00017041124,0.00019232524,0.00016046432],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00083003764,0.0002544496,0.00025607238,0.000303818,0.00090569677,0.0042301733,0.0003802179,0.000060812217,0.0000047343874],"category_scores_gemma":[0.0002991384,0.00020506099,0.00006238627,0.00032357502,0.00012856637,0.0037468509,0.00016200796,0.000492556,0.000003860127],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030321386,0.00002566577,0.00013863905,0.0005968703,0.000015657086,0.00011840612,0.00083078886,0.0000029237642,0.041932814,0.0020963897,0.0013860547,0.9528255],"study_design_scores_gemma":[0.0016328101,0.0010054021,0.0009479284,0.0071528233,0.000112149806,0.0038391063,0.00020809459,0.5755936,0.056040134,0.31654677,0.0354273,0.0014939103],"about_ca_topic_score_codex":0.000001352104,"about_ca_topic_score_gemma":6.070406e-7,"teacher_disagreement_score":0.95133156,"about_ca_system_score_codex":0.000045960598,"about_ca_system_score_gemma":0.000122009005,"threshold_uncertainty_score":0.9968035},"labels":[],"label_agreement":null},{"id":"W4404797443","doi":"10.1016/j.procs.2024.09.190","title":"SIR-SRGAN-ResNeXt: A New Super-Resolution GAN with Self-Interpolation Ranker and ResNeXt Generator","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Université du Québec à Chicoutimi","keywords":"Computer science; Generator (circuit theory); Interpolation (computer graphics); Algorithm; Physics; Telecommunications; Frame (networking); Quantum mechanics","score_opus":0.009607950074009754,"score_gpt":0.24980798241963115,"score_spread":0.2402000323456214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404797443","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006060299,0.0013177661,0.98823845,0.0012980227,0.00045649864,0.0003393595,0.0000011512635,0.0021232713,0.00016516117],"genre_scores_gemma":[0.44641975,0.000023526061,0.55292517,0.00031646743,0.0002346396,0.0000195312,6.815968e-7,0.000017611252,0.000042634132],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99693656,0.00003465036,0.00030221965,0.0013741194,0.00074510503,0.0006073658],"domain_scores_gemma":[0.9985761,0.000087126245,0.00008532325,0.0006128299,0.0003147799,0.0003238186],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00059675454,0.00031384794,0.00021474439,0.00046658335,0.00041497964,0.0022218118,0.0013706251,0.00007209328,0.0000024397307],"category_scores_gemma":[0.00005995394,0.00025653935,0.000035904774,0.0022381644,0.00036431864,0.005120698,0.00060198153,0.0002789518,0.000024443205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004102108,0.00012937945,0.00043270478,0.00037123298,0.00003500912,0.00012286076,0.010167762,0.0001560657,0.26417312,0.01821696,0.002053332,0.70410055],"study_design_scores_gemma":[0.00020918381,0.00021968536,0.00043688205,0.00018269225,0.000010077964,0.00024153086,0.0000057789703,0.9501126,0.041298155,0.0043585617,0.002550202,0.00037467468],"about_ca_topic_score_codex":0.00001839216,"about_ca_topic_score_gemma":0.000014674064,"teacher_disagreement_score":0.94995654,"about_ca_system_score_codex":0.00014534693,"about_ca_system_score_gemma":0.0008245129,"threshold_uncertainty_score":0.9999887},"labels":[],"label_agreement":null},{"id":"W4405105327","doi":"10.1016/j.geomat.2024.100042","title":"FocalSR: Revisiting image super-resolution transformers with fourier-transform cross attention layers for remote sensing image enhancement","year":2024,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Food and Agriculture","keywords":"Image (mathematics); Fourier transform; Computer science; Computer vision; Artificial intelligence; Remote sensing; Superresolution; Physics; Geology","score_opus":0.011312798802344605,"score_gpt":0.2990639960665507,"score_spread":0.2877511972642061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405105327","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025711092,0.00028117147,0.990083,0.0035689143,0.00014566466,0.00086009863,0.000010021027,0.001166952,0.0013130823],"genre_scores_gemma":[0.053043272,0.0000410707,0.9464151,0.00012504915,0.00009877131,0.000020401936,0.00001848386,0.00004703781,0.00019078983],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771297,0.00003784241,0.00049634883,0.00066471146,0.00043360054,0.0006545141],"domain_scores_gemma":[0.9990144,0.00013747126,0.00008891552,0.0004092868,0.00024582783,0.00010414226],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000789357,0.0002947257,0.0002739575,0.00017089046,0.00041071858,0.00097441854,0.00036284176,0.000079273326,0.000008488183],"category_scores_gemma":[0.00007366292,0.00025839114,0.00015208258,0.00048098847,0.0001709119,0.0024251165,0.00005763295,0.00020549496,0.000024512805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003420678,0.0000143145335,0.000001171899,0.0013452206,0.000042392992,0.00003892868,0.00066728564,0.0000102628055,0.124512605,0.0011002178,0.00020183926,0.87203157],"study_design_scores_gemma":[0.00037562934,0.00023266938,0.00000924045,0.0014236931,0.000049909704,0.00009328994,0.0001023082,0.9105971,0.06610266,0.018144138,0.002468163,0.00040121732],"about_ca_topic_score_codex":0.000019867804,"about_ca_topic_score_gemma":0.0000060555803,"teacher_disagreement_score":0.91058683,"about_ca_system_score_codex":0.00020552013,"about_ca_system_score_gemma":0.00011477502,"threshold_uncertainty_score":0.9999868},"labels":[],"label_agreement":null},{"id":"W4405365671","doi":"10.1016/j.imavis.2024.105364","title":"CLBSR: A deep curriculum learning-based blind image super resolution network using geometrical prior","year":2024,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Toronto","funders":"","keywords":"Artificial intelligence; Image (mathematics); Computer science; Superresolution; Deep learning; Curriculum; Computer vision; Resolution (logic); Psychology","score_opus":0.015052725529519338,"score_gpt":0.34420698800963373,"score_spread":0.32915426248011437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405365671","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015894264,0.0036118925,0.97810996,0.00035737836,0.00030534645,0.00019584931,4.984951e-7,0.0014057227,0.00011906935],"genre_scores_gemma":[0.3226437,0.00002141526,0.6768939,0.00012938368,0.00026470597,0.0000024062444,0.0000031882382,0.000027082777,0.000014235257],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975281,0.00017187398,0.00043275717,0.0008650022,0.0003967914,0.00060551043],"domain_scores_gemma":[0.9987619,0.00038711278,0.0001222726,0.0003475487,0.00023478575,0.00014635893],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0010076447,0.00027717228,0.0002791226,0.00044382946,0.0007055301,0.0016087628,0.00047736478,0.000114256996,0.000008456552],"category_scores_gemma":[0.00037209844,0.00025454603,0.00010380335,0.002007652,0.00014145633,0.0015040708,0.00074819033,0.00063126144,0.000021579423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039535447,0.00022341343,0.0011580343,0.000491757,0.000034283836,0.00053013384,0.0006829947,0.0150574455,0.025707692,0.0011085385,0.0023951465,0.95257103],"study_design_scores_gemma":[0.0003036597,0.00014017856,0.00019698778,0.0005228781,0.000015260084,0.00008070015,0.000016857775,0.9939481,0.00084651064,0.001329354,0.0022803647,0.00031917394],"about_ca_topic_score_codex":0.000018993558,"about_ca_topic_score_gemma":7.1003194e-7,"teacher_disagreement_score":0.97889066,"about_ca_system_score_codex":0.000102044534,"about_ca_system_score_gemma":0.00010010652,"threshold_uncertainty_score":0.9999907},"labels":[],"label_agreement":null},{"id":"W4405376223","doi":"10.1145/3708347","title":"Neural Image Compression with Regional Decoding","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Decoding methods; Image compression; Image (mathematics); Computer vision; Data compression; Compression (physics); Artificial intelligence; Image processing; Algorithm","score_opus":0.030001178440056914,"score_gpt":0.32704059917831324,"score_spread":0.29703942073825634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405376223","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001925496,0.0010830021,0.9896162,0.0068231868,0.000039122682,0.0004112441,0.000008786334,0.0015214706,0.00030441766],"genre_scores_gemma":[0.2966987,0.00036005655,0.70247054,0.00017021217,0.000026761541,0.00020453228,0.000014403009,0.00002079396,0.000034015477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986973,0.00007310901,0.0002817873,0.0005188229,0.00019303052,0.00023597215],"domain_scores_gemma":[0.99629396,0.0010741984,0.000078611505,0.002294855,0.00014044683,0.00011794467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017797652,0.00020845688,0.00015589336,0.00025717702,0.0011148741,0.00045045867,0.0018888463,0.00006159199,0.0000045899787],"category_scores_gemma":[0.00001286956,0.00018463499,0.000054484037,0.00079965655,0.00034302182,0.00066088117,0.00016684119,0.0005265388,0.000022748967],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035112298,0.00014820223,0.000013558306,0.000036835954,0.000024771682,0.0000016788808,0.0004956462,0.00018392684,0.0041080336,0.0069240183,0.000096325304,0.9879635],"study_design_scores_gemma":[0.00016892469,0.000052542273,0.00008547689,0.00021314743,0.000022741684,0.000080946484,0.00006280663,0.9775892,0.0008123347,0.005247815,0.015413189,0.0002509077],"about_ca_topic_score_codex":0.000015237136,"about_ca_topic_score_gemma":0.0000069038515,"teacher_disagreement_score":0.98771256,"about_ca_system_score_codex":0.000052416166,"about_ca_system_score_gemma":0.000068359885,"threshold_uncertainty_score":0.8574823},"labels":[],"label_agreement":null},{"id":"W4406160139","doi":"10.1016/j.eswa.2025.126416","title":"Inter-frame residual frequency-based reconstruction learning for deep video frame interpolation detection","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"Guangdong Provincial Key Laboratory of Robotics and Intelligent Systems; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Frame (networking); Residual; Artificial intelligence; Residual frame; Computer vision; Interpolation (computer graphics); Deep learning; Motion interpolation; Pattern recognition (psychology); Reference frame; Algorithm; Video tracking; Video processing; Telecommunications; Block-matching algorithm","score_opus":0.008576228013502676,"score_gpt":0.2788852912507866,"score_spread":0.27030906323728393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406160139","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010622367,0.0004865053,0.99552506,0.0006035864,0.00022754059,0.0013370094,0.0000018970775,0.0012363652,0.0004758348],"genre_scores_gemma":[0.40645218,0.0000043248892,0.5882771,0.00011666328,0.00009131522,0.0049212356,0.000011617343,0.000016865843,0.00010869487],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985584,0.00007999363,0.00040038835,0.0005803913,0.00014954331,0.00023128372],"domain_scores_gemma":[0.998426,0.00020898432,0.0002942681,0.0006167543,0.00040145149,0.000052516978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024865006,0.00018886311,0.00019700048,0.00032282714,0.0004720106,0.00029829648,0.0004951055,0.00013277584,0.000001683332],"category_scores_gemma":[0.00012383463,0.00017943792,0.000046417244,0.0006876472,0.00008360024,0.0007097191,0.00005858347,0.00023915603,0.000007029049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012482931,0.00022878665,0.0020999955,0.00056926045,0.00013465741,0.0000013870634,0.0019763452,0.0024414905,0.24874708,0.08997808,0.00094062876,0.65275747],"study_design_scores_gemma":[0.00049422315,0.00020382847,0.000056483244,0.000549655,0.000015456159,0.000035546003,0.0004156652,0.9354264,0.024719518,0.01595031,0.02174886,0.00038404236],"about_ca_topic_score_codex":0.00007735944,"about_ca_topic_score_gemma":0.000034368946,"teacher_disagreement_score":0.93298495,"about_ca_system_score_codex":0.00023294004,"about_ca_system_score_gemma":0.00013560243,"threshold_uncertainty_score":0.7317266},"labels":[],"label_agreement":null},{"id":"W4406195432","doi":"10.18280/ts.410627","title":"Recent Advances in Image Super-Resolution Reconstruction Based on Machine Learning","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Superresolution; Computer science; Artificial intelligence; Image (mathematics); Computer vision; Pattern recognition (psychology); Machine learning","score_opus":0.01228203657303537,"score_gpt":0.2674615139026576,"score_spread":0.2551794773296222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406195432","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000993707,0.0033285054,0.9916825,0.0013494679,0.00022443387,0.00018578136,0.0000027988794,0.00089209375,0.0013406745],"genre_scores_gemma":[0.3775189,0.0012995737,0.6205981,0.000284804,0.00010556972,0.00009097371,0.00001842873,0.000026434927,0.00005724385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858665,0.00010618089,0.00027656343,0.00047343574,0.000294995,0.00026219073],"domain_scores_gemma":[0.9995949,0.000089669506,0.000052271025,0.000162148,0.00005440494,0.000046552745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004608311,0.00016469229,0.00012733096,0.00031953858,0.00010468963,0.00021476955,0.0002915214,0.00004073693,0.00011781504],"category_scores_gemma":[0.000043984495,0.00015505045,0.000042385946,0.0005448456,0.000053235708,0.0015679188,0.000052364256,0.0003187601,0.000023135564],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027641912,0.00006405344,0.00020219838,0.000054628672,0.0000025381194,0.00003184291,0.00011412647,0.0036535545,0.014465978,0.0016902941,0.000069426365,0.97962373],"study_design_scores_gemma":[0.0002490865,0.0002092531,0.000113942275,0.00028982817,0.0000030500591,0.000015337944,0.0000095765945,0.9562161,0.007812066,0.0036899466,0.03121521,0.00017663203],"about_ca_topic_score_codex":0.0000040471086,"about_ca_topic_score_gemma":0.000008893916,"teacher_disagreement_score":0.97944707,"about_ca_system_score_codex":0.00021594655,"about_ca_system_score_gemma":0.000065475135,"threshold_uncertainty_score":0.6322774},"labels":[],"label_agreement":null},{"id":"W4406377584","doi":"10.2174/0118722121297704240917145311","title":"High Resolution Medical Image Inpainting Based on Super Resolution","year":2025,"lang":"en","type":"article","venue":"Recent Patents on Engineering","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Inpainting; Artificial intelligence; Computation; Image (mathematics); Computer science; Similarity (geometry); Process (computing); Computer vision; Feature (linguistics); Image resolution; Scale (ratio); Residual; Feature extraction; Pattern recognition (psychology); Image restoration; Resolution (logic); Image processing; Algorithm; Geography","score_opus":0.00964266490756679,"score_gpt":0.2507881880526546,"score_spread":0.24114552314508783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406377584","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021325082,0.00006191924,0.9930744,0.0022396878,0.00053505256,0.00015806268,0.0000013190606,0.0012087135,0.0005883405],"genre_scores_gemma":[0.37932935,0.00010612831,0.6193253,0.0010009042,0.000072740506,0.000050720628,0.000013319162,0.000028905779,0.000072671464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821985,0.000054289634,0.00029821132,0.00046442173,0.00056362624,0.0003996118],"domain_scores_gemma":[0.999116,0.00011753569,0.00005838442,0.00049858104,0.00011396581,0.00009554604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005682075,0.00020267106,0.00017253007,0.000372955,0.00011650545,0.000104051855,0.00060935406,0.00012250584,0.000013707714],"category_scores_gemma":[0.0009633004,0.0002006095,0.000047115973,0.0006744438,0.00002363836,0.0003943473,0.00017620464,0.00038625216,0.000019684907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000211259,0.0012349855,0.00081991364,0.0007217239,0.000084085455,0.0002477689,0.00018356334,0.22998637,0.09278274,0.08639917,0.0064246347,0.58090377],"study_design_scores_gemma":[0.00034599355,0.00005985263,0.0007483947,0.00079322816,0.0000032771823,0.0000014910097,0.0000013499987,0.9744716,0.01966303,0.00044559242,0.0032805302,0.00018560667],"about_ca_topic_score_codex":0.000008291268,"about_ca_topic_score_gemma":4.6347486e-7,"teacher_disagreement_score":0.74448526,"about_ca_system_score_codex":0.00032635024,"about_ca_system_score_gemma":0.00006661807,"threshold_uncertainty_score":0.8180618},"labels":[],"label_agreement":null},{"id":"W4407901240","doi":"10.1016/j.patcog.2025.111491","title":"Refining attention weights for facial super-resolution with counterfactual attention learning","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Counterfactual thinking; Refining (metallurgy); Computer science; Artificial intelligence; Resolution (logic); Face (sociological concept); Pattern recognition (psychology); Computer vision; Psychology; Social psychology; Chemistry; Linguistics","score_opus":0.021818510389999445,"score_gpt":0.2773361031655743,"score_spread":0.25551759277557484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407901240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08751755,0.000039420825,0.91050017,0.00038308091,0.00023840305,0.0002531362,0.000009618748,0.00065212254,0.0004064809],"genre_scores_gemma":[0.7702784,0.0000122870915,0.22853655,0.00021679427,0.00008646445,0.00024096802,0.00022826495,0.000020006742,0.0003802892],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985978,0.000075793476,0.0002794084,0.00052212965,0.00022207276,0.0003027733],"domain_scores_gemma":[0.99916005,0.00008633088,0.00017722795,0.00019477305,0.00034653855,0.000035104444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024470987,0.0001877224,0.00016467935,0.00025545814,0.00040680642,0.00024977126,0.00025574162,0.000096492266,0.0000073383176],"category_scores_gemma":[0.00007339535,0.00018051466,0.00006653933,0.0002835304,0.000045208166,0.001298359,0.00008639954,0.00020969505,0.000030797106],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006580517,0.000069075126,0.006120402,0.00013166237,0.000027208062,0.0000027216088,0.000142323,0.000013533506,0.021492062,0.00015110147,0.00014773419,0.97163635],"study_design_scores_gemma":[0.007818533,0.002534638,0.06308766,0.005745677,0.0003303693,0.00012518292,0.00044964073,0.77490413,0.071270876,0.054133855,0.016885828,0.0027136314],"about_ca_topic_score_codex":0.000024439423,"about_ca_topic_score_gemma":0.000027475648,"teacher_disagreement_score":0.96892273,"about_ca_system_score_codex":0.00013507738,"about_ca_system_score_gemma":0.000046393237,"threshold_uncertainty_score":0.7361174},"labels":[],"label_agreement":null},{"id":"W4407937179","doi":"10.2139/ssrn.5143616","title":"Blind Super-Resolution Network for Thyroid Ultrasound Image Based on Multiple Degradation Model","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Degradation (telecommunications); Computer science; Ultrasound; Image (mathematics); Computer vision; Artificial intelligence; Radiology; Medicine; Telecommunications","score_opus":0.017673714523327954,"score_gpt":0.28975334917601847,"score_spread":0.2720796346526905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407937179","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00034211623,0.0018520873,0.9947154,0.0011687571,0.0004149105,0.00078827335,0.000028123419,0.00046016142,0.00023015446],"genre_scores_gemma":[0.070360124,0.0012063321,0.92642605,0.00036352215,0.0004187911,0.00019159769,0.000111315254,0.00004635288,0.00087588583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99537057,0.00018839473,0.0005918507,0.0008756359,0.00050315476,0.0024704111],"domain_scores_gemma":[0.99736357,0.00056460494,0.0005466398,0.000890738,0.00053633226,0.00009810234],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0028398442,0.00048596493,0.0004162413,0.00036424765,0.0006287975,0.0005824706,0.0019832791,0.00034543738,0.000001062941],"category_scores_gemma":[0.0007575832,0.00048504042,0.0003173024,0.00035634503,0.00006776209,0.00070927356,0.00035954287,0.0039086957,0.000002420218],"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":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004948063,0.00038958792,0.00013431338,0.00018967976,0.00017388,0.0000034519546,0.00015344132,0.8419246,0.004587283,0.090337336,0.0039529847,0.05765861],"study_design_scores_gemma":[0.0005784155,0.00015443056,0.000004517775,0.00015365629,0.000026314214,0.000018525277,0.000008109071,0.5640717,0.00060939445,0.43397865,0.00013785587,0.0002584237],"about_ca_topic_score_codex":0.000019431589,"about_ca_topic_score_gemma":0.00014189265,"teacher_disagreement_score":0.3436413,"about_ca_system_score_codex":0.0023444896,"about_ca_system_score_gemma":0.007076403,"threshold_uncertainty_score":0.99976015},"labels":[],"label_agreement":null},{"id":"W4408354201","doi":"10.1109/icassp49660.2025.10889642","title":"LFSRDiff: Light Field Image Super-Resolution via Diffusion Models","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Research and Development","keywords":"Computer science; Image resolution; Diffusion; Light field; Field (mathematics); Computer vision; Resolution (logic); Artificial intelligence; Image (mathematics); Physics; Mathematics","score_opus":0.007261473242202874,"score_gpt":0.26309203570054507,"score_spread":0.2558305624583422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408354201","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001807042,0.00017670899,0.95164937,0.006733736,0.00015240564,0.00012881002,2.9016897e-7,0.0012045081,0.039773464],"genre_scores_gemma":[0.14757971,0.000022901635,0.8474777,0.0018855018,0.000020775653,0.000025319601,0.0000012229535,0.0000068526256,0.0029800064],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989356,0.000029284474,0.00020181804,0.00041140098,0.00017539343,0.00024653142],"domain_scores_gemma":[0.99909246,0.0000619585,0.000039021805,0.00064084574,0.00011907786,0.00004664595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009786269,0.00014244382,0.00013645159,0.00016095967,0.00017665807,0.00016701309,0.0008452601,0.00008372069,0.000015782502],"category_scores_gemma":[0.000052632324,0.00012298275,0.000053967946,0.00043514225,0.00002829693,0.0015558486,0.0006448998,0.00015240659,0.000021645596],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015569734,0.0001875817,0.000045502627,0.00006422318,0.000010490575,0.000016496235,0.00024763265,0.000039354476,0.5546088,0.23317806,0.03297237,0.17861393],"study_design_scores_gemma":[0.000107291184,0.000029134413,0.000024596873,0.00003830647,0.0000027003757,0.0000035863527,0.000004164297,0.5957729,0.12521395,0.27681467,0.0018630454,0.00012567328],"about_ca_topic_score_codex":0.000039655006,"about_ca_topic_score_gemma":0.0000061934056,"teacher_disagreement_score":0.5957335,"about_ca_system_score_codex":0.00005453469,"about_ca_system_score_gemma":0.000043239488,"threshold_uncertainty_score":0.50150913},"labels":[],"label_agreement":null},{"id":"W4409189862","doi":"10.1007/s10489-025-06490-6","title":"DCSR: A deep continual learning-based scheme for image super resolution using knowledge distillation","year":2025,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Toronto","funders":"","keywords":"Computer science; Scheme (mathematics); Artificial intelligence; Resolution (logic); Distillation; Image (mathematics); Computer vision; Machine learning; Chromatography","score_opus":0.020836230279012335,"score_gpt":0.3348811723632545,"score_spread":0.31404494208424216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409189862","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006419965,0.00032227623,0.99519414,0.00012048815,0.00011854563,0.00050628465,0.0000014008161,0.0006813173,0.0024135779],"genre_scores_gemma":[0.40128368,0.0000045459533,0.5983927,0.000076987584,0.000025579111,0.00009234525,0.0000060348875,0.000011739067,0.00010638243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852806,0.000034849403,0.00034950796,0.00058201276,0.00014523037,0.00036036212],"domain_scores_gemma":[0.9987856,0.00025911524,0.00013429878,0.0004211116,0.00035053302,0.000049335915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038971382,0.00020332179,0.00020191965,0.00019932484,0.00032998386,0.00019556211,0.00067778275,0.00010116138,0.0000059440295],"category_scores_gemma":[0.000330821,0.00021622101,0.0000664511,0.00069945864,0.00016023217,0.00043655638,0.00022139281,0.00021646969,0.00001965266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001560443,0.00024429744,0.00021567446,0.00036526987,0.000033162793,0.000003231308,0.0010026972,0.00815819,0.31669462,0.32737404,0.0006180242,0.34513476],"study_design_scores_gemma":[0.00010080363,0.000031979285,0.000018168417,0.00006727151,0.000007607673,0.0000011550751,0.000033235898,0.7932513,0.17795712,0.024652377,0.003688411,0.00019057767],"about_ca_topic_score_codex":0.0000065400714,"about_ca_topic_score_gemma":0.00000500451,"teacher_disagreement_score":0.7850931,"about_ca_system_score_codex":0.00017206422,"about_ca_system_score_gemma":0.00017030258,"threshold_uncertainty_score":0.8817237},"labels":[],"label_agreement":null},{"id":"W4409870592","doi":"10.1177/14727978241299518","title":"A recognition method of COVID-19 CT image based on ResNet network","year":2024,"lang":"en","type":"article","venue":"Journal of Computational Methods in Sciences and Engineering","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Residual neural network; Coronavirus disease 2019 (COVID-19); Computer science; Artificial intelligence; Image (mathematics); Pattern recognition (psychology); Computer vision; Artificial neural network; Medicine; Pathology","score_opus":0.05762497404024221,"score_gpt":0.44074441064623965,"score_spread":0.38311943660599745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409870592","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028557124,0.0007229072,0.9977678,0.0007935055,0.00025574848,0.000042316882,0.0000010916444,0.000048075235,0.0000829665],"genre_scores_gemma":[0.009489824,0.000024823305,0.99025524,0.00016584851,0.000055673554,0.0000021791466,2.1842915e-7,0.0000049617847,0.000001237082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878186,0.00017957482,0.00039962315,0.00018332129,0.00031875452,0.00013683736],"domain_scores_gemma":[0.99687487,0.0027329638,0.0001559679,0.00006235027,0.00009588103,0.00007796415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057144077,0.00008883466,0.0001881937,0.00051632145,0.000051645213,0.00015227792,0.00031672054,0.000018818508,0.0000026406672],"category_scores_gemma":[0.0009483126,0.00007397179,0.000049005845,0.0010840913,0.000068087706,0.00064920465,0.000052918516,0.00019889267,1.5705774e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052704636,0.000009671106,0.000016310474,0.00009518559,0.0000039365605,0.000029508532,0.000101042286,0.86311024,0.0012594452,0.0043518525,0.000054170418,0.13096339],"study_design_scores_gemma":[0.000081413236,0.000115928764,0.00011984612,0.00030181996,0.0000030430604,0.00014353212,0.000008093759,0.8118645,0.0006822045,0.18627872,0.00033506594,0.00006582558],"about_ca_topic_score_codex":0.000002380027,"about_ca_topic_score_gemma":1.0769815e-7,"teacher_disagreement_score":0.18192686,"about_ca_system_score_codex":0.000055035423,"about_ca_system_score_gemma":0.00025007204,"threshold_uncertainty_score":0.3016482},"labels":[],"label_agreement":null},{"id":"W4411284470","doi":"10.1007/s11042-025-20946-4","title":"HiSpecmer: a deep efficient image super resolution network using transformers with hierarchical and spectral feature attention","year":2025,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Toronto","funders":"","keywords":"Computer science; Transformer; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Image (mathematics); Superresolution; Computer vision; Voltage; Electrical engineering","score_opus":0.00876387843133815,"score_gpt":0.2613785485364546,"score_spread":0.25261467010511646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411284470","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006551903,0.0006427075,0.99011093,0.0015987895,0.000020477863,0.0005329507,0.0000059822255,0.00019546693,0.00034076232],"genre_scores_gemma":[0.07444734,0.00010157377,0.9250434,0.00011287074,0.000059353013,0.00016414332,0.000013928199,0.000008305163,0.00004909315],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990853,0.000021700913,0.00013380575,0.00039806412,0.00011485836,0.0002462898],"domain_scores_gemma":[0.9995535,0.00006562528,0.0000412934,0.00020555974,0.00006124641,0.000072747214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000108183405,0.00013209892,0.0001283875,0.00007187398,0.00036302072,0.00019574366,0.00016265363,0.00006184366,0.0000010649912],"category_scores_gemma":[0.000009819749,0.00011239823,0.000024249355,0.00044167976,0.00021442883,0.00032842343,0.000071059934,0.00019289425,8.554669e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007351161,0.0002498214,0.0020444458,0.00014478636,0.000052575782,0.000006373266,0.0007106122,0.0010464679,0.087770246,0.05122421,0.00043114624,0.8562458],"study_design_scores_gemma":[0.00046640943,0.000035314366,0.009010634,0.00008350527,0.000033229273,0.000025176483,0.000034036617,0.98093957,0.0009162928,0.004715471,0.0035278567,0.0002124798],"about_ca_topic_score_codex":0.0000066077814,"about_ca_topic_score_gemma":0.000008468449,"teacher_disagreement_score":0.97989315,"about_ca_system_score_codex":0.000046895784,"about_ca_system_score_gemma":0.00004257234,"threshold_uncertainty_score":0.45834666},"labels":[],"label_agreement":null},{"id":"W4411989738","doi":"10.18280/isi.300521","title":"Enhancing Medical Image Instance Segmentation Using Histogram Equalization and Blind Deblurring: A Preliminary Study","year":2025,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Histogram equalization; Deblurring; Adaptive histogram equalization; Artificial intelligence; Computer vision; Histogram matching; Computer science; Balanced histogram thresholding; Segmentation; Pattern recognition (psychology); Histogram; Equalization (audio); Image segmentation; Color normalization; Image histogram; Image (mathematics); Image processing; Image restoration; Image texture; Channel (broadcasting); Color image","score_opus":0.01697132460177714,"score_gpt":0.3100250295520353,"score_spread":0.2930537049502582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411989738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11652314,0.00019616718,0.8814794,0.000041514715,0.00014963192,0.00054116244,7.8853304e-7,0.00047011473,0.0005980712],"genre_scores_gemma":[0.627605,0.000013108881,0.3721171,0.00016595062,0.000011190572,0.00006341067,0.000008144754,0.000005874889,0.000010185313],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983613,0.00009734182,0.0006631246,0.00023243927,0.0004153353,0.00023045341],"domain_scores_gemma":[0.99891615,0.00007913807,0.0003320108,0.0002881977,0.0003176734,0.00006681773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073374854,0.0001767163,0.00018638183,0.00045294687,0.00041603926,0.00054184673,0.00038630582,0.00009300074,0.0000029813145],"category_scores_gemma":[0.00066203007,0.00018602714,0.000023797495,0.00085504306,0.000120173056,0.00814912,0.00033356986,0.00014612461,0.0000026705427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016906291,0.0003079927,0.0059783314,0.0016635596,0.00007176344,0.000021035454,0.04775844,0.00035705493,0.014231387,0.013080435,0.00010010559,0.91626084],"study_design_scores_gemma":[0.0018494108,0.00042318326,0.0028680423,0.0014724652,0.000047856163,0.00008157068,0.0041784244,0.9449986,0.022491306,0.020751443,0.00027030695,0.00056736154],"about_ca_topic_score_codex":0.000041125746,"about_ca_topic_score_gemma":0.0000124820035,"teacher_disagreement_score":0.9446416,"about_ca_system_score_codex":0.0003956536,"about_ca_system_score_gemma":0.00022980176,"threshold_uncertainty_score":0.75859666},"labels":[],"label_agreement":null},{"id":"W4411996053","doi":"10.1109/tase.2025.3585728","title":"Automated Live Cell Evaluation via a CNN-Transformer Combined Microscopy Image Enhancement Network","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CReATe Fertility Centre; University of Toronto","funders":"","keywords":"Artificial intelligence; Microscopy; Computer vision; Transformer; Computer science; Materials science; Biomedical engineering; Engineering; Electrical engineering; Voltage; Optics; Physics","score_opus":0.007491334443541025,"score_gpt":0.28148691705178436,"score_spread":0.27399558260824336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411996053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072850753,0.00006328778,0.98938614,0.00027102916,0.00053066516,0.00042550726,9.3412314e-7,0.0017141646,0.00032319923],"genre_scores_gemma":[0.633165,0.000025087504,0.36650681,0.0001366649,0.0000058749915,0.000112578055,6.604575e-7,0.000006608328,0.000040722793],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983405,0.00002382797,0.00029493688,0.00044857446,0.00053685514,0.0003553182],"domain_scores_gemma":[0.9990723,0.00006189662,0.000069373884,0.00032583298,0.00039478444,0.000075828524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094362564,0.00018328699,0.00014828944,0.00043648045,0.00048088797,0.0003629373,0.00043528236,0.000055117358,0.000011451271],"category_scores_gemma":[0.000018734881,0.00018946902,0.000033267097,0.0017922096,0.000116525545,0.0019265956,0.0000055788973,0.00015926556,0.000019440004],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007024492,0.00007831288,0.0000013385493,0.00007207337,0.000008740125,7.5874914e-7,0.0005108848,0.04263034,0.9060148,0.00017971358,0.00014617018,0.050349858],"study_design_scores_gemma":[0.00021180253,0.000044821707,0.00008149972,0.00008267811,0.000010232865,0.0000017004838,0.000008620197,0.6449497,0.35415855,0.00029107745,0.00004216199,0.000117167256],"about_ca_topic_score_codex":0.000006705553,"about_ca_topic_score_gemma":0.0000010485305,"teacher_disagreement_score":0.6258799,"about_ca_system_score_codex":0.00027237402,"about_ca_system_score_gemma":0.00024815885,"threshold_uncertainty_score":0.77263224},"labels":[],"label_agreement":null},{"id":"W4412017870","doi":"10.1093/mnras/staf1096","title":"The Close AGN Reference Survey (CARS): a comparison between sub-mm and optical AGN diagnostic diagrams","year":2025,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"H2020 European Research Council; Natural Sciences and Engineering Research Council of Canada; Science and Technology Facilities Council; UK Research and Innovation","keywords":"Physics; Astrophysics; Astronomy","score_opus":0.016898244504328495,"score_gpt":0.26887835134350035,"score_spread":0.2519801068391719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412017870","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91607875,0.0010105468,0.08043677,0.0016084244,0.00015173796,0.00036145846,0.000031637195,0.00014333228,0.00017736565],"genre_scores_gemma":[0.91590005,0.0000035955024,0.083882645,0.00006077247,0.000038721548,0.000032342134,0.000005444094,0.0000109190805,0.0000654818],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979244,0.00026525662,0.00052917516,0.00053141953,0.000273907,0.00047588226],"domain_scores_gemma":[0.9940415,0.0046177953,0.000286454,0.0008214212,0.00012181003,0.00011103415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096606475,0.00025709177,0.00042319705,0.000015736283,0.00047924469,0.00031682514,0.0021861047,0.00014159734,8.009609e-7],"category_scores_gemma":[0.0010062556,0.00016566589,0.00018758985,0.00022716424,0.0007971222,0.0002416803,0.0018231543,0.0005595452,0.0000026827192],"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":[0.000051030474,0.00029665622,0.8191418,0.00009332115,0.00022029493,4.0273628e-7,0.00043139857,0.02170567,0.00007959892,0.0034004317,0.0043398254,0.15023956],"study_design_scores_gemma":[0.00023557781,0.000074504154,0.6891342,0.00008004748,0.00004145116,1.0613917e-8,0.00002757179,0.30621406,0.0019020826,0.001561643,0.0005655775,0.00016327141],"about_ca_topic_score_codex":0.0002340248,"about_ca_topic_score_gemma":0.00010826045,"teacher_disagreement_score":0.28450838,"about_ca_system_score_codex":0.000111356254,"about_ca_system_score_gemma":0.000103271814,"threshold_uncertainty_score":0.67556584},"labels":[],"label_agreement":null},{"id":"W4412451826","doi":"10.1016/j.bspc.2025.108236","title":"Blind super-resolution network for thyroid ultrasound image based on multiple degradation model","year":2025,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"University of Science and Technology Liaoning; National Natural Science Foundation of China","keywords":"Computer science; Degradation (telecommunications); Image (mathematics); Ultrasound; Artificial intelligence; Computer vision; Radiology; Medicine; Telecommunications","score_opus":0.01249099360836252,"score_gpt":0.27103610000658845,"score_spread":0.2585451063982259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412451826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028599333,0.0007228051,0.9952562,0.0027603568,0.000055857574,0.00039609673,0.000011393537,0.00040471557,0.00010661456],"genre_scores_gemma":[0.59386957,0.0000043373902,0.4046112,0.0012306058,0.00007680913,0.00011666157,0.000018019662,0.000008606164,0.00006422155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983903,0.0000644989,0.00030410924,0.0005313282,0.00030636214,0.00040340357],"domain_scores_gemma":[0.99868315,0.0006858647,0.00011310853,0.000198137,0.0001971424,0.00012259097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006282331,0.00020378153,0.00023263643,0.00014763742,0.0005111231,0.00034123653,0.00039811124,0.00014165865,0.0000013661202],"category_scores_gemma":[0.0004120092,0.0001726277,0.0000582666,0.00042923092,0.0002184246,0.00048314384,0.00004291673,0.00018203634,8.68206e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008286719,0.00051512953,0.00026572583,0.0003924981,0.000031701235,0.0000049371197,0.000105653475,0.0046155727,0.17831618,0.002449969,0.00583247,0.8066415],"study_design_scores_gemma":[0.0025192057,0.00018693302,0.000053045573,0.00020988124,0.000022464252,0.0000018917132,0.0000052541236,0.9714988,0.0014738308,0.022959193,0.00088658504,0.00018294872],"about_ca_topic_score_codex":0.0000049783953,"about_ca_topic_score_gemma":0.0000012761883,"teacher_disagreement_score":0.9668832,"about_ca_system_score_codex":0.00006150399,"about_ca_system_score_gemma":0.0002918063,"threshold_uncertainty_score":0.7039553},"labels":[],"label_agreement":null},{"id":"W4412531504","doi":"10.1016/j.engappai.2025.111706","title":"Super-resolution reconstruction of WorldView-3 multispectral satellite images based on generative adversarial networks","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Ontario Ministry of Natural Resources and Forestry","keywords":"Computer science; Multispectral image; Adversarial system; Generative grammar; Artificial intelligence; Satellite; Superresolution; Generative adversarial network; Computer vision; Resolution (logic); Remote sensing; Image (mathematics); Pattern recognition (psychology); Geology","score_opus":0.011219735300539614,"score_gpt":0.2723580835264879,"score_spread":0.2611383482259483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412531504","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018506088,0.00031163468,0.99830717,0.0002156993,0.00018346982,0.00034053024,0.00000482134,0.00024423256,0.00020736076],"genre_scores_gemma":[0.43489632,0.00004183739,0.5648988,0.000014926726,0.000036804846,0.0000911857,0.0000032901735,0.0000060081948,0.000010838247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988207,0.000027192924,0.00048670758,0.0003412447,0.00014270034,0.00018144256],"domain_scores_gemma":[0.9988374,0.00021943224,0.00015195315,0.0005268307,0.00023091095,0.00003343943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022374697,0.00014881448,0.00019388266,0.00033917517,0.00008257539,0.0000376879,0.0005308629,0.000069617054,0.0000035946425],"category_scores_gemma":[0.0001340402,0.0001651502,0.00007073769,0.0010934183,0.00012280024,0.00025454458,0.00006535038,0.00017858377,0.000002653957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017448205,0.00008778832,0.000020639689,0.000039630748,0.000010362159,2.3827873e-7,0.00006241237,0.43957058,0.041726712,0.07179779,0.000020703084,0.4466457],"study_design_scores_gemma":[0.000015589316,0.000027040182,0.000041416093,0.00008469087,0.000005290893,5.364221e-7,0.000010455902,0.6596048,0.33642995,0.003549197,0.00014771108,0.00008334661],"about_ca_topic_score_codex":0.000018338023,"about_ca_topic_score_gemma":0.000003553508,"teacher_disagreement_score":0.44656235,"about_ca_system_score_codex":0.00007531733,"about_ca_system_score_gemma":0.000065237364,"threshold_uncertainty_score":0.673463},"labels":[],"label_agreement":null},{"id":"W4412909022","doi":"10.1093/mam/ozaf048.915","title":"Towards Cinematic STEM and Beyond: Fast Frame Rates Using Overdriven Scan Shaping","year":2025,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Frame (networking); Materials science; Computer science; Telecommunications","score_opus":0.016843798479579654,"score_gpt":0.31525548704792855,"score_spread":0.2984116885683489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412909022","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0988508,0.0058532576,0.89427644,0.0004692161,0.000044927976,0.00011930434,0.000004894213,0.00016537323,0.00021579838],"genre_scores_gemma":[0.24913771,0.00023996897,0.7493526,0.0009299239,0.000013036813,0.000009014724,0.0000024733254,0.0000130183425,0.00030224666],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985994,0.00005784013,0.0003350935,0.00059235335,0.000109684304,0.00030563885],"domain_scores_gemma":[0.9991823,0.000055602497,0.00015438456,0.0004268718,0.000109509485,0.00007134246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024971875,0.0002411869,0.0004276941,0.00039178328,0.0003667749,0.0005695613,0.00045051856,0.000084317304,0.0000047073718],"category_scores_gemma":[0.00002686727,0.0002294813,0.00007301905,0.0008566061,0.00016668058,0.0006224131,0.0005788558,0.00016432069,0.0000012974173],"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":[0.00000635767,0.000030116833,0.0015875085,0.00023247492,0.00013730135,0.000008074417,0.00073491596,0.000013192454,0.94471323,0.000957742,0.000144934,0.051434122],"study_design_scores_gemma":[0.0005741383,0.000055912955,0.00075910194,0.0007596813,0.00041289645,0.00006411797,0.00051408046,0.21847244,0.7579563,0.018889979,0.0008320142,0.0007093408],"about_ca_topic_score_codex":0.00007828942,"about_ca_topic_score_gemma":0.000011050001,"teacher_disagreement_score":0.21845925,"about_ca_system_score_codex":0.00007320309,"about_ca_system_score_gemma":0.00009745773,"threshold_uncertainty_score":0.9357976},"labels":[],"label_agreement":null},{"id":"W4413011405","doi":"10.1051/0004-6361/202554947","title":"Investigation on deep learning-based galaxy image translation models","year":2025,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Peng Cheng Laboratory","keywords":"Physics; Astrophysics; Galaxy; Astronomy; Translation (biology); Image (mathematics); Artificial intelligence","score_opus":0.015356282485473495,"score_gpt":0.23477940311532505,"score_spread":0.21942312062985156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413011405","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023075987,0.000039755396,0.9962922,0.00043060296,0.000028275519,0.00010301348,6.5777016e-7,0.0002643402,0.00053358567],"genre_scores_gemma":[0.35037273,0.0000012517492,0.64949346,0.00007694589,0.000013816003,0.000014042695,0.0000067248034,0.00000474652,0.00001626568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992974,0.0000387751,0.00012564649,0.00028830508,0.000092391965,0.00015747764],"domain_scores_gemma":[0.99959505,0.00004406505,0.000070807546,0.00019647928,0.000053696996,0.000039922423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000058721776,0.00012786133,0.00010465419,0.00007996508,0.00016531868,0.00014276912,0.00020663544,0.000029882884,7.196718e-7],"category_scores_gemma":[0.0000061498727,0.00013484179,0.000031593874,0.00021596174,0.00007194492,0.0009132294,0.00004207298,0.00018302746,0.0000029904136],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009071687,0.000016176822,0.00008304816,0.000014105637,0.000005326493,4.68735e-7,0.00009031086,0.013539067,0.0053258897,0.016854778,0.000012372467,0.9640494],"study_design_scores_gemma":[0.00033755825,0.00014160652,0.00024523764,0.00007415696,0.000008431482,2.2581754e-7,0.000015280722,0.875481,0.04210901,0.079981685,0.0014494788,0.0001562946],"about_ca_topic_score_codex":0.0000024149701,"about_ca_topic_score_gemma":2.5065327e-7,"teacher_disagreement_score":0.9638931,"about_ca_system_score_codex":0.00002474121,"about_ca_system_score_gemma":0.000052937783,"threshold_uncertainty_score":0.5498688},"labels":[],"label_agreement":null},{"id":"W4413145364","doi":"10.1109/cvpr52734.2025.00360","title":"VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by VIdeo SpatioTemporal Augmentation","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Duration (music); Computer science","score_opus":0.016028838484531568,"score_gpt":0.2835980329974205,"score_spread":0.2675691945128889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413145364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016321517,0.00022136298,0.9941722,0.0023966194,0.0001381034,0.00019755375,9.869185e-7,0.0005994973,0.00064152514],"genre_scores_gemma":[0.63227004,0.00002801508,0.36700773,0.0002950161,0.000011557588,0.000015308337,0.000011422842,0.000005204962,0.00035571025],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99885345,0.000048837668,0.00032592067,0.00039986186,0.0001867241,0.00018519706],"domain_scores_gemma":[0.9994348,0.000080318474,0.00014933867,0.00022486097,0.000072057526,0.00003856717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003111267,0.0001311923,0.00011837357,0.00017014508,0.0002872481,0.00038766774,0.00020414131,0.00006051137,0.000004879669],"category_scores_gemma":[0.000076097145,0.00013459627,0.000017284468,0.0004120963,0.000054726424,0.0020642509,0.00015205349,0.000093817805,0.0000025010222],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056753637,0.00011187249,0.0020706735,0.00027456591,0.00004436263,0.00000812837,0.00090287946,0.00008798407,0.38473728,0.5134322,0.02069671,0.0775766],"study_design_scores_gemma":[0.00072959706,0.00011609298,0.0010823879,0.00026299426,0.000019124944,0.0000061523765,0.00018093282,0.32062948,0.4324377,0.24368726,0.00043419137,0.0004140832],"about_ca_topic_score_codex":0.00010682045,"about_ca_topic_score_gemma":0.00009341012,"teacher_disagreement_score":0.6306379,"about_ca_system_score_codex":0.00040506624,"about_ca_system_score_gemma":0.00006499163,"threshold_uncertainty_score":0.54886764},"labels":[],"label_agreement":null},{"id":"W4413146637","doi":"10.1109/cvpr52734.2025.00679","title":"BiM-VFI: Bidirectional Motion Field-Guided Frame Interpolation for Video with Non-uniform Motions","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Interpolation (computer graphics); Motion (physics); Frame (networking); Motion interpolation; Computer science; Computer graphics (images); Field (mathematics); Computer vision; Block-matching algorithm; Video processing; Mathematics; Video tracking; Telecommunications","score_opus":0.014343005544895152,"score_gpt":0.30488967007514567,"score_spread":0.2905466645302505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413146637","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00031520665,0.00001207602,0.988892,0.0027402462,0.00019660503,0.00031602682,0.0000014219198,0.0007401589,0.006786244],"genre_scores_gemma":[0.1313195,0.0000024376616,0.86628914,0.0010851002,0.000030172489,0.00012593305,0.0000050601925,0.0000067432184,0.001135916],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991708,0.000011271965,0.00020072133,0.00032153123,0.00012667444,0.00016903444],"domain_scores_gemma":[0.9991548,0.000112264664,0.0000806789,0.00032204695,0.00029812235,0.00003205876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014166365,0.00011796096,0.00010854961,0.00025684352,0.00020684312,0.00015510805,0.00035672545,0.000059603943,0.000014746063],"category_scores_gemma":[0.00014283044,0.000099666635,0.000044652712,0.0005575614,0.000028589979,0.0010628925,0.0001082654,0.000109459914,0.000004876069],"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":[0.00007738514,0.00043690542,0.002765065,0.00025462217,0.000113940274,0.0000033674798,0.0004618706,0.00081822823,0.038142286,0.6542941,0.044209335,0.25842294],"study_design_scores_gemma":[0.00031196073,0.00014697138,0.0005034264,0.00011641127,0.000009300718,0.000010608447,0.000017499173,0.7954276,0.04990121,0.15126681,0.0021219861,0.00016619338],"about_ca_topic_score_codex":0.00003291898,"about_ca_topic_score_gemma":0.000023476456,"teacher_disagreement_score":0.79460937,"about_ca_system_score_codex":0.00007325616,"about_ca_system_score_gemma":0.000094658906,"threshold_uncertainty_score":0.40642875},"labels":[],"label_agreement":null},{"id":"W4413278547","doi":"10.1109/icip55913.2025.11084315","title":"Enhanced Multi-Scale Network for Single Image Super-Resolution","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Scale (ratio); Image (mathematics); Image resolution; Resolution (logic); Computer vision; Artificial intelligence; Cartography; Geography","score_opus":0.017869811642589505,"score_gpt":0.3024228554989615,"score_spread":0.284553043856372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413278547","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006982121,0.00018401223,0.99071944,0.0008488971,0.000244835,0.0003217761,0.0000012462287,0.0012423164,0.0063676387],"genre_scores_gemma":[0.027043546,0.000005478514,0.9689805,0.00068564725,0.000046424022,0.000112957925,0.0000031057582,0.000009018125,0.0031133601],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99893266,0.000021589265,0.00019851167,0.0004068992,0.00008942105,0.00035089315],"domain_scores_gemma":[0.9991763,0.00009341835,0.000048520513,0.00045160754,0.0001927553,0.000037345955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018014688,0.00012683719,0.00014103115,0.00006696964,0.00020153783,0.00016609515,0.00065062253,0.000058997874,0.0000046218156],"category_scores_gemma":[0.00009582652,0.00012081174,0.00005968658,0.000431211,0.00006112918,0.00080051273,0.00026074747,0.000075601325,0.000008865337],"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":[0.000014334158,0.00016217768,0.000023968167,0.000052410873,0.0000081218,9.551235e-7,0.00011531508,0.00010074083,0.88605446,0.018624686,0.013813417,0.081029415],"study_design_scores_gemma":[0.00037109116,0.00006349921,0.000056605222,0.00006774381,0.0000050455074,0.0000015645535,0.000009653727,0.42297766,0.5315335,0.038764246,0.0059566293,0.00019276822],"about_ca_topic_score_codex":0.000007261979,"about_ca_topic_score_gemma":0.000019073035,"teacher_disagreement_score":0.42287692,"about_ca_system_score_codex":0.0000742631,"about_ca_system_score_gemma":0.00005314168,"threshold_uncertainty_score":0.492656},"labels":[],"label_agreement":null},{"id":"W4414153667","doi":"10.1007/s11760-025-04537-2","title":"Enhanced image restoration via MmiRNet with context-aware deep learning inpainting","year":2025,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Inpainting; Image restoration; Deep learning; Preprocessor; Histogram equalization; Benchmark (surveying); Convolutional neural network; Image quality; Pattern recognition (psychology)","score_opus":0.006933117513158815,"score_gpt":0.26776895607252366,"score_spread":0.2608358385593648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414153667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024555805,0.0012370052,0.9931629,0.00047837663,0.000027414395,0.00020515842,2.8183274e-7,0.00085870695,0.0015746128],"genre_scores_gemma":[0.632789,0.000014760253,0.36647338,0.00045013946,0.000031823944,0.0000357843,0.0000033034185,0.000018314655,0.00018350992],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99808353,0.0001322735,0.00036702177,0.0007188661,0.0002659606,0.00043234794],"domain_scores_gemma":[0.9986777,0.00014312308,0.00029788757,0.0002571902,0.0005411111,0.00008299644],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00057834445,0.00029752104,0.0002951512,0.00025053235,0.000750649,0.0011983726,0.0004651185,0.00008985154,0.000005994434],"category_scores_gemma":[0.00019317253,0.000268445,0.00003454003,0.0007939845,0.00024447168,0.0042965445,0.00028124332,0.0004717378,0.0000048395764],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003480502,0.000025604504,0.00011313012,0.00025155235,0.000010077322,0.000030329315,0.00091736356,0.000016094777,0.32534474,0.00024229295,0.000037050107,0.672977],"study_design_scores_gemma":[0.0007625749,0.00021546023,0.0002188808,0.0014746129,0.000036085014,0.00005411058,0.0006577129,0.44525933,0.5369536,0.013154262,0.00055719406,0.00065614464],"about_ca_topic_score_codex":0.000017138791,"about_ca_topic_score_gemma":0.0000124605795,"teacher_disagreement_score":0.67232084,"about_ca_system_score_codex":0.000071928036,"about_ca_system_score_gemma":0.0001704876,"threshold_uncertainty_score":0.99997675},"labels":[],"label_agreement":null},{"id":"W4414689366","doi":"10.1088/1475-7516/2025/10/004","title":"DESI DR1 Lyα 1D power spectrum: the optimal estimator measurement","year":2025,"lang":"en","type":"article","venue":"Journal of Cosmology and Astroparticle Physics","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Perimeter Institute; University of Waterloo; University of Toronto","funders":"","keywords":"Estimator; Dark energy; Spectral density; Dark matter; Pipeline (software); Baryon; Measure (data warehouse); Quadratic equation; COSMIC cancer database","score_opus":0.013470973696407033,"score_gpt":0.2672363124089124,"score_spread":0.25376533871250534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414689366","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08752953,0.0004206166,0.9073985,0.004362009,0.0001274503,0.000048362755,1.5088872e-7,0.000031350242,0.00008200202],"genre_scores_gemma":[0.7559241,0.000006804398,0.24376942,0.00025859257,0.000029760224,0.0000021126164,1.8331034e-8,0.0000031104842,0.0000060551],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991559,0.00006413139,0.00025372714,0.0001233126,0.0002001411,0.00020281532],"domain_scores_gemma":[0.99925435,0.00007265856,0.00020303176,0.00021064091,0.00020905021,0.000050264163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048609803,0.000097619966,0.00016943316,0.000034767017,0.00019649374,0.000081299884,0.00046193885,0.000024240628,0.000001514161],"category_scores_gemma":[0.00008496597,0.000066982975,0.000049576363,0.0002004515,0.00017172654,0.00041974548,0.00015867781,0.00023464533,0.00000169795],"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":[0.00037493155,0.00091303815,0.021092027,0.000050844297,0.0003646666,0.00019731895,0.0010037709,0.0024331466,0.12170876,0.6542463,0.00531568,0.19229956],"study_design_scores_gemma":[0.00090054394,0.00065887946,0.010708786,0.00011628443,0.00006540169,0.00027462974,0.000072212075,0.02889514,0.24534604,0.7120057,0.0007655121,0.00019092593],"about_ca_topic_score_codex":0.000001104638,"about_ca_topic_score_gemma":3.4241722e-7,"teacher_disagreement_score":0.66839457,"about_ca_system_score_codex":0.000039432292,"about_ca_system_score_gemma":0.00011905348,"threshold_uncertainty_score":0.27314866},"labels":[],"label_agreement":null},{"id":"W4415707828","doi":"10.1109/tbc.2025.3622337","title":"IRBFusion: Diffusion-Based Blind Image Super Resolution Using Unsupervised Learning and Bank of Restoration Networks","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Broadcasting","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image restoration; Benchmark (surveying); Unsupervised learning; Feature (linguistics); Image (mathematics); Image resolution; Feature detection (computer vision); Process (computing); Pattern recognition (psychology)","score_opus":0.02485249570092648,"score_gpt":0.2979972159722782,"score_spread":0.2731447202713517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415707828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034350928,0.0011738709,0.9621749,0.00028411846,0.0008177206,0.0005904168,0.00000682072,0.00040239998,0.00019881393],"genre_scores_gemma":[0.66441,0.00018348011,0.33507758,0.00008665928,0.00005491616,0.000021192247,0.0000027247459,0.000037706,0.00012574658],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959896,0.0005314014,0.0011447209,0.0011278449,0.0005482883,0.0006581174],"domain_scores_gemma":[0.99724853,0.00072684535,0.0005114618,0.0006355892,0.0007240232,0.00015353422],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00090312975,0.00055153674,0.0005835902,0.0010100398,0.0019474342,0.00043590076,0.00048499086,0.00039330122,0.000043987184],"category_scores_gemma":[0.00018306448,0.0006377675,0.00018612322,0.0024150836,0.00048814144,0.0014496415,0.000032022723,0.0013008362,0.0000017999371],"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":[0.00046439172,0.00038875078,0.0001612948,0.00043502368,0.00004130269,0.000014786447,0.0006718301,0.39141002,0.25223425,0.000049226244,0.000012177156,0.35411695],"study_design_scores_gemma":[0.0015180714,0.0002859055,0.00010304912,0.0028708768,0.00013729735,0.000016055626,0.0001337396,0.9540313,0.040220175,0.0001583537,0.00006632855,0.00045885236],"about_ca_topic_score_codex":0.00015057655,"about_ca_topic_score_gemma":0.000018677327,"teacher_disagreement_score":0.63005906,"about_ca_system_score_codex":0.00032442764,"about_ca_system_score_gemma":0.0004842529,"threshold_uncertainty_score":0.9996074},"labels":[],"label_agreement":null},{"id":"W4415743467","doi":"10.1109/tpami.2025.3627285","title":"Structural Similarity in Deep Features: Unified Image Quality Assessment Robust to Geometrically Disparate Reference","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Robustness (evolution); Pixel; Image quality; Similarity (geometry); Image (mathematics); Pattern recognition (psychology); Standard test image","score_opus":0.04030313243363029,"score_gpt":0.35872911519690925,"score_spread":0.31842598276327894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415743467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030114113,0.0000737523,0.99513334,0.0010867901,0.000083846564,0.00020349705,0.000028384202,0.00016345581,0.0002155233],"genre_scores_gemma":[0.8238514,0.000113790775,0.17510477,0.00075307017,0.0000033305814,0.00003788701,0.000004682521,0.000006175588,0.00012489273],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977376,0.00019102414,0.00057065644,0.0008439588,0.00032601488,0.00033073762],"domain_scores_gemma":[0.9985351,0.00026628003,0.00012610613,0.0007542061,0.00018487511,0.00013340953],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004891766,0.00028554318,0.0004677876,0.0014066616,0.00022438267,0.00033520788,0.0008903732,0.0000911068,0.00003685748],"category_scores_gemma":[0.000037959457,0.00025188588,0.00013383981,0.004528716,0.000078220335,0.00047574745,0.00003233154,0.0006106837,0.000003640024],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000349911,0.00020234555,0.0030592093,0.000059703027,0.0001951407,0.000016135022,0.00022949897,0.05172882,0.0018527687,0.00088630273,0.000008499013,0.94172657],"study_design_scores_gemma":[0.00023137094,0.00018193905,0.11546814,0.000113006405,0.00024493967,0.0000051315305,0.00008371322,0.7904724,0.084136754,0.00824247,0.000031653162,0.000788485],"about_ca_topic_score_codex":0.0017246412,"about_ca_topic_score_gemma":0.005482672,"teacher_disagreement_score":0.9409381,"about_ca_system_score_codex":0.00015309316,"about_ca_system_score_gemma":0.000056536726,"threshold_uncertainty_score":0.9999933},"labels":[],"label_agreement":null},{"id":"W4416429117","doi":"10.1109/tetci.2025.3631689","title":"UMIC: Super-Resolution of Cine Cardiac MRI Using U-Shaped Network With Multi-Level Information Compensation","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Feature (linguistics); Channel (broadcasting); Feature extraction; Compensation (psychology); Pattern recognition (psychology); Iterative reconstruction; Compressed sensing; Data compression; Information hiding","score_opus":0.056941898412426056,"score_gpt":0.34009344892994287,"score_spread":0.28315155051751684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416429117","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001345217,0.00028719462,0.9949096,0.000899439,0.0015232561,0.00075419084,0.00004437609,0.00016325946,0.000073459945],"genre_scores_gemma":[0.47665533,0.00012400158,0.52293456,0.00014953093,0.00003516862,0.000027550992,0.000011831426,0.0000123301625,0.00004967005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996444,0.00024029562,0.0014638419,0.00062957604,0.00070722343,0.0005150942],"domain_scores_gemma":[0.9972265,0.00042245517,0.00053919974,0.0005294673,0.0012044522,0.00007795244],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006727311,0.00045258037,0.0005529168,0.0009969895,0.00051424175,0.00017624891,0.00074381585,0.00021663123,0.000018010236],"category_scores_gemma":[0.00004103819,0.0005100322,0.00015286318,0.0027949507,0.00037503932,0.0020670977,0.00002556059,0.00072876154,0.0000065328],"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":[0.00009900003,0.00022127292,0.00015582642,0.00019161376,0.00006818127,0.0000018877874,0.000990633,0.88723296,0.00017677523,0.007146102,0.000012209417,0.103703566],"study_design_scores_gemma":[0.00035932314,0.00015656448,0.0008173735,0.0015217437,0.000060536353,0.0000063141974,0.00014472776,0.98068213,0.0054586525,0.0102617135,0.0001152135,0.00041570564],"about_ca_topic_score_codex":0.0002143608,"about_ca_topic_score_gemma":0.0000719611,"teacher_disagreement_score":0.47531012,"about_ca_system_score_codex":0.00057278824,"about_ca_system_score_gemma":0.0006663616,"threshold_uncertainty_score":0.9997351},"labels":[],"label_agreement":null},{"id":"W4416468328","doi":"","title":"Imagerie vibratoire de plein champ : vers l'utilisation de caméras de vision industrielle ou de téléphones cellulaires pour l'application des méthodes de grille","year":2025,"lang":"fr","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Context (archaeology); Field (mathematics); Stage (stratigraphy)","score_opus":0.01861073638792096,"score_gpt":0.27638740543945034,"score_spread":0.2577766690515294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416468328","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06504413,0.004323372,0.89253736,0.03173443,0.00011813377,0.0004332361,0.000023162684,0.0006075384,0.0051786527],"genre_scores_gemma":[0.41216522,0.0009391936,0.5792132,0.0003743279,0.0000337758,0.0000857382,0.00004643075,0.00004391168,0.0070981723],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9903331,0.0065409862,0.0006572325,0.00095617794,0.00038948335,0.0011230336],"domain_scores_gemma":[0.9935566,0.002116028,0.00054542476,0.0017498553,0.0016487793,0.00038326962],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.009133222,0.00045983863,0.00037891787,0.00035202288,0.0011052175,0.0009823778,0.0019856705,0.000502238,0.00007252624],"category_scores_gemma":[0.004270039,0.00057159615,0.00017280073,0.0015605824,0.0008683371,0.0013264078,0.00090797455,0.0007262141,0.00003738812],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005291733,0.0009778676,0.01750144,0.00048398064,0.000058582737,0.00003291305,0.039770566,0.00054159336,0.27280018,0.10210789,0.0052277255,0.56044436],"study_design_scores_gemma":[0.0004635591,0.000002493877,0.0062875873,0.001764653,0.00003604558,0.00006555636,0.0009000808,0.38679144,0.50895816,0.084208176,0.010100644,0.00042161057],"about_ca_topic_score_codex":0.0056348857,"about_ca_topic_score_gemma":0.0016544089,"teacher_disagreement_score":0.5600227,"about_ca_system_score_codex":0.0017543589,"about_ca_system_score_gemma":0.003417122,"threshold_uncertainty_score":0.99967355},"labels":[],"label_agreement":null},{"id":"W4416798771","doi":"10.1109/icriset64803.2025.11251840","title":"Resource Efficient Image Super-Resolution for FPGA-Based Optimized Deep Learning – An Innovative Target Detection Model","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Deep learning; Field-programmable gate array; Convolutional neural network; Process (computing); Inference; Edge device; Image (mathematics); Enhanced Data Rates for GSM Evolution; Artificial neural network","score_opus":0.014671888793155707,"score_gpt":0.29718738213997303,"score_spread":0.2825154933468173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416798771","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007777038,0.00029234003,0.9920187,0.0014558665,0.00023369485,0.001681398,0.000008699442,0.0019618613,0.0015697469],"genre_scores_gemma":[0.26612917,0.000004214935,0.7318826,0.0007102603,0.000040925344,0.00032074846,0.000028047178,0.00005007014,0.00083396997],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950669,0.00042576122,0.001050583,0.0018237748,0.00055246695,0.0010805079],"domain_scores_gemma":[0.9955029,0.00035209497,0.0004985622,0.0010508699,0.0024257284,0.00016985039],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001825361,0.0006694473,0.0006271653,0.0010353914,0.0016235854,0.0008844956,0.0013602095,0.00034656216,0.000016368307],"category_scores_gemma":[0.0012936668,0.0007190146,0.00019659827,0.0035093995,0.0004182638,0.0016729995,0.00059187965,0.0008614837,0.000005335498],"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":[0.00046163224,0.00035964244,0.0000020065593,0.00014824397,0.000022745726,0.0000019591953,0.00088828977,0.8250222,0.06728301,0.0015638791,0.00010883751,0.1041375],"study_design_scores_gemma":[0.0015991617,0.00045274317,0.000003076953,0.00015711335,0.000028500235,0.0000019447605,0.00014548455,0.73411566,0.25739196,0.0043826345,0.0011891793,0.00053253054],"about_ca_topic_score_codex":0.000028003009,"about_ca_topic_score_gemma":0.0000060930192,"teacher_disagreement_score":0.26535147,"about_ca_system_score_codex":0.0007095441,"about_ca_system_score_gemma":0.0006541561,"threshold_uncertainty_score":0.99967617},"labels":[],"label_agreement":null},{"id":"W4416918118","doi":"10.1016/j.asoc.2025.114315","title":"Multi-scale implicit diffusion model with high-frequency uncertainty guidance and global structure consistency","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Sichuan Provincial Youth Science and Technology Fund","keywords":"Consistency (knowledge bases); Prior probability; Convergence (economics); Representation (politics); Feature (linguistics); Process (computing); Image (mathematics); Probabilistic logic; Simple (philosophy)","score_opus":0.007106248282283055,"score_gpt":0.25746912574869285,"score_spread":0.2503628774664098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416918118","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049789265,0.0002784351,0.9475,0.00028416354,0.000060890416,0.00031240168,0.000008160871,0.0008609959,0.00090569037],"genre_scores_gemma":[0.49025184,0.0000023596876,0.509245,0.0004617977,0.000009455285,0.0000068585286,0.0000022915465,0.000007593945,0.00001280665],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806696,0.000024170364,0.00035050118,0.0008716297,0.00023181611,0.00045489165],"domain_scores_gemma":[0.99878025,0.00009486051,0.00020217804,0.0006669841,0.00016619805,0.00008952373],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001464041,0.00031938552,0.00034163892,0.00007215,0.00050634646,0.0002172758,0.00085243897,0.00011586049,5.758624e-7],"category_scores_gemma":[0.000034847435,0.00027879814,0.000028939226,0.0006282875,0.00020458525,0.00024492064,0.00086399395,0.000261195,8.3917297e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006514232,0.0002012139,0.014726157,0.00041741642,0.0000754464,0.000024207768,0.0012442662,0.034549497,0.28784105,0.37088102,0.00023122557,0.28974336],"study_design_scores_gemma":[0.0006470969,0.000020909547,0.003052334,0.00015334666,0.000012451761,0.000018158524,0.00002811027,0.8520654,0.0013130604,0.14235668,0.000011988643,0.0003205082],"about_ca_topic_score_codex":0.00006261234,"about_ca_topic_score_gemma":0.000038235623,"teacher_disagreement_score":0.81751585,"about_ca_system_score_codex":0.00014746063,"about_ca_system_score_gemma":0.00018298478,"threshold_uncertainty_score":0.99996644},"labels":[],"label_agreement":null},{"id":"W4416962404","doi":"10.1109/embc58623.2025.11252891","title":"Resolution Enhancement of Prostate 3D MRI and Ultrasound Using Implicit Neural Representations","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; Hamilton Health Sciences; McMaster University","funders":"","keywords":"Convolutional neural network; Visibility; Deep learning; Medical imaging; Magnetic resonance imaging; Leverage (statistics); Artificial neural network; Image quality","score_opus":0.016727026775830163,"score_gpt":0.3313863862168853,"score_spread":0.31465935944105516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416962404","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017997093,0.00018210031,0.979854,0.00035667475,0.000041771164,0.00017439033,7.947772e-7,0.00021118659,0.0011820176],"genre_scores_gemma":[0.2927031,0.000028222568,0.70693755,0.00010043391,0.0000032681762,0.000010202523,6.206056e-7,0.0000028992797,0.00021371541],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930775,0.000023375824,0.00019410104,0.0002504193,0.00009566295,0.000128678],"domain_scores_gemma":[0.9994345,0.000063391184,0.00008036626,0.00029352732,0.00010874024,0.000019490704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010996528,0.000067972374,0.00008679801,0.00009882245,0.0001055829,0.00007070072,0.00019666205,0.000018589262,0.0000015971053],"category_scores_gemma":[0.00005844879,0.000063921136,0.000013808888,0.0003523461,0.000069025715,0.0005699319,0.00019357099,0.00005027952,2.9711438e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012391035,0.0000675725,0.0028151874,0.00008908833,0.000015307272,0.0000016656155,0.00058162515,0.00031253742,0.9328161,0.03433459,0.00056390656,0.02839007],"study_design_scores_gemma":[0.00019970021,0.00005261718,0.0015923012,0.000076338816,0.000009938294,0.0000138777805,0.00004759151,0.49851373,0.45374137,0.045423426,0.00019761676,0.00013148929],"about_ca_topic_score_codex":0.000060292223,"about_ca_topic_score_gemma":0.0000040203395,"teacher_disagreement_score":0.4982012,"about_ca_system_score_codex":0.00003205946,"about_ca_system_score_gemma":0.000042145613,"threshold_uncertainty_score":0.26066282},"labels":[],"label_agreement":null},{"id":"W4416979199","doi":"10.1007/s00530-025-02047-2","title":"Opfusion: a deep blind image super resolution network using generative diffusion models and neural operator learning","year":2025,"lang":"en","type":"article","venue":"Multimedia Systems","topic":"Advanced Image Processing 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":"Canada Research Chairs; University of Toronto; Concordia University","funders":"","keywords":"Feature (linguistics); Image (mathematics); Pattern recognition (psychology); Operator (biology); Deep learning; Image restoration; Superresolution; Artificial neural network; Process (computing)","score_opus":0.02727858047469842,"score_gpt":0.2954313513702432,"score_spread":0.26815277089554473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416979199","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018315725,0.0036386838,0.97609913,0.00016688602,0.00066172716,0.0005125207,0.0000010261278,0.00045970638,0.00014457888],"genre_scores_gemma":[0.25824296,0.0000554564,0.7411204,0.00010295713,0.00021532939,0.000059952043,0.000004810517,0.000018095368,0.00018006733],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980357,0.0003005152,0.00038031588,0.00061591854,0.00026057378,0.00040694495],"domain_scores_gemma":[0.99900967,0.00012853194,0.00013106756,0.00037389883,0.0002541086,0.000102742124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004326397,0.0002459374,0.00031333257,0.00016275303,0.00062713685,0.00053990213,0.00042190411,0.00013166381,0.0000014687365],"category_scores_gemma":[0.0001242536,0.00022106704,0.00003979158,0.0005439682,0.00010788413,0.0014312371,0.00064908253,0.000317325,0.000001776308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001615183,0.00019295813,0.002616613,0.00045047625,0.000084332496,0.00013292978,0.010193066,0.31526572,0.5417495,0.0025374424,0.001347311,0.12526813],"study_design_scores_gemma":[0.0005358748,0.000034607434,0.00004151738,0.00025499228,0.000010226903,0.000024556066,0.00010433069,0.9972023,0.0007858736,0.0005303872,0.00025307012,0.00022225764],"about_ca_topic_score_codex":0.00010496726,"about_ca_topic_score_gemma":0.000007154976,"teacher_disagreement_score":0.68193656,"about_ca_system_score_codex":0.00012482754,"about_ca_system_score_gemma":0.00007942074,"threshold_uncertainty_score":0.9014852},"labels":[],"label_agreement":null},{"id":"W4417000867","doi":"10.1145/3763306","title":"Generating the Past, Present and Future from a Motion-Blurred Image","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto; York University","funders":"","keywords":"Leverage (statistics); Prior probability; Image (mathematics); Moment (physics); Robustness (evolution); Object (grammar); Image restoration; Motion blur; Optical flow","score_opus":0.011319873760340259,"score_gpt":0.2696492367496998,"score_spread":0.25832936298935955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417000867","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006328833,0.0004514362,0.95918006,0.03889546,0.00022996997,0.00015440362,0.000009350636,0.00035748226,0.00008896456],"genre_scores_gemma":[0.05703526,0.00034926328,0.9405472,0.0016446135,0.00022558654,0.00009608423,0.000002960222,0.000011634476,0.00008735071],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99911356,0.00006449312,0.00015962802,0.00034592993,0.00016043079,0.00015598082],"domain_scores_gemma":[0.9987258,0.00021647323,0.00005042866,0.0008751099,0.00009719468,0.000035009965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011746051,0.00013497255,0.00009935592,0.00013991784,0.00057201664,0.00026396386,0.0007736503,0.000066401786,0.000003772118],"category_scores_gemma":[0.000013327251,0.000103223094,0.000055908567,0.00067574315,0.000114597286,0.0004741573,0.000035681114,0.0003353896,0.0000012619378],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000128931215,0.00017521622,0.00013133661,0.000036426383,0.00008445748,0.0000066927632,0.0009740991,0.0001881132,0.0078842165,0.007936145,0.0023852424,0.98018515],"study_design_scores_gemma":[0.0008201417,0.00011058096,0.0029404727,0.00015155834,0.000106430394,0.00001680217,0.000317147,0.43246546,0.022372095,0.500466,0.03961393,0.00061939907],"about_ca_topic_score_codex":0.000026623677,"about_ca_topic_score_gemma":0.00001027601,"teacher_disagreement_score":0.97956574,"about_ca_system_score_codex":0.000016062697,"about_ca_system_score_gemma":0.000032986198,"threshold_uncertainty_score":0.43995473},"labels":[],"label_agreement":null},{"id":"W4417002662","doi":"10.1109/access.2025.3640134","title":"DDR-Net: Dual-Stream for Degraded Infrared Image Restoration Network","year":2025,"lang":"","type":"article","venue":"IEEE Access","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Kitakyushu Foundation for the Advancement of Industry, Science and Technology; Korea Institute for Advancement of Technology; Human Resources Research Institute","keywords":"Noise (video); Benchmark (surveying); Image restoration; Noise reduction; Projection (relational algebra); Object detection; Key (lock); Enhanced Data Rates for GSM Evolution","score_opus":0.0332721182892136,"score_gpt":0.35766731499208665,"score_spread":0.32439519670287303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417002662","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00066995795,0.0021324074,0.985471,0.0019446403,0.0035331273,0.0016338786,0.000026915899,0.0010197159,0.0035683387],"genre_scores_gemma":[0.094406016,0.00030258708,0.8963954,0.0022416874,0.0010585734,0.0007458713,0.000033142598,0.000078227145,0.004738481],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99580336,0.0001913492,0.0010509692,0.0014123316,0.00045650214,0.0010855079],"domain_scores_gemma":[0.99589396,0.0004802482,0.0007184728,0.0017237655,0.0010131943,0.00017034008],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007357296,0.0006127536,0.00063271023,0.0004037865,0.0009648907,0.003321641,0.0032118645,0.00035723168,0.000015409301],"category_scores_gemma":[0.00055298867,0.00067970506,0.00021024489,0.0027096658,0.00034121677,0.0069789616,0.0009229097,0.0004949822,0.000016915183],"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":[0.00045972792,0.0005880557,0.001312403,0.0013308526,0.00021412053,0.00007870128,0.000963283,0.0019283249,0.027421448,0.015171173,0.5054372,0.44509473],"study_design_scores_gemma":[0.0016014306,0.00030189005,0.0010639743,0.0013815106,0.00014792432,0.000016589918,0.000020058484,0.36237797,0.12284909,0.474062,0.03494381,0.0012337349],"about_ca_topic_score_codex":0.000037321446,"about_ca_topic_score_gemma":0.000030832216,"teacher_disagreement_score":0.47049338,"about_ca_system_score_codex":0.00030780977,"about_ca_system_score_gemma":0.0008309664,"threshold_uncertainty_score":0.9995654},"labels":[],"label_agreement":null},{"id":"W4417337851","doi":"10.1109/iccubea65967.2025.11283668","title":"AI-Powered Satellite Images Enhancement","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Geospatial analysis; Satellite; Cloud computing; Satellite imagery; Ground truth; Scalability; Image (mathematics); Deep learning","score_opus":0.009061637901619603,"score_gpt":0.31430608476751287,"score_spread":0.30524444686589325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417337851","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007274668,0.015377657,0.84969205,0.015807277,0.00096804474,0.00044267008,0.0000019716133,0.00089590676,0.11680713],"genre_scores_gemma":[0.06384841,0.0049772137,0.8668303,0.011469151,0.000053821845,0.00006793239,0.0000021365622,0.00002259119,0.052728463],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9963493,0.000115300376,0.0008312808,0.0013570364,0.00046463558,0.0008824691],"domain_scores_gemma":[0.99725264,0.000107541775,0.00022412122,0.0017146469,0.0005556196,0.00014542055],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005007599,0.0005216936,0.00047746327,0.00039484655,0.0003846105,0.0011620431,0.0022677754,0.00016549224,0.00034818775],"category_scores_gemma":[0.00014073995,0.000516239,0.00015714292,0.0016010335,0.00035715435,0.0020107187,0.0017453005,0.00046021736,0.0002749391],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022077156,0.00026499803,0.000061855644,0.00019679326,0.000050740964,0.000023006245,0.00023314542,7.1842874e-7,0.06872234,0.054049775,0.011369602,0.86500496],"study_design_scores_gemma":[0.00035352202,0.0001480537,0.00013279948,0.0005626309,0.000029209488,0.000005082761,0.00002294278,0.018340912,0.6962016,0.1421308,0.14149375,0.00057863933],"about_ca_topic_score_codex":0.000017949364,"about_ca_topic_score_gemma":0.0000027507956,"teacher_disagreement_score":0.8644263,"about_ca_system_score_codex":0.00024345044,"about_ca_system_score_gemma":0.00053017837,"threshold_uncertainty_score":0.99987483},"labels":[],"label_agreement":null},{"id":"W4417400950","doi":"10.5194/ica-abs-10-314-2025","title":"A fast twin modelling method for earthquake AR scenes with multi-sensor perceptual enhancement","year":2025,"lang":"en","type":"article","venue":"Abstracts of the ICA","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; China Scholarship Council","keywords":"Perception; Object (grammar); Noise (video); Feature (linguistics); Field (mathematics)","score_opus":0.027255760476567515,"score_gpt":0.31603772107534833,"score_spread":0.2887819605987808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417400950","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008980489,0.000090499794,0.9888675,0.0011316279,0.00007464455,0.00038607666,0.0000031358722,0.00012963804,0.00033634834],"genre_scores_gemma":[0.31152743,0.0000060456937,0.68767834,0.00019203662,0.000010342664,0.000033415294,3.8862035e-7,0.0000077664145,0.0005442492],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988917,0.000034513116,0.00028160127,0.00034569122,0.00020046323,0.00024603168],"domain_scores_gemma":[0.9987645,0.00018796386,0.000194275,0.0006193485,0.00020061349,0.00003327109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031534128,0.00015047027,0.00019506422,0.00006812351,0.00015654755,0.00006930274,0.0009469881,0.000047388054,0.0000017065308],"category_scores_gemma":[0.00008153884,0.00010211202,0.00007339044,0.00021317345,0.000091379894,0.0003056439,0.00020737,0.00013350185,0.0000017547727],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002771866,0.0010519316,0.00013601509,0.0006277047,0.00020809945,0.000007724087,0.004332898,0.17162979,0.321013,0.012036483,0.0007732384,0.48790592],"study_design_scores_gemma":[0.00045287548,0.00010058105,0.00054265215,0.0003431475,0.000024379682,0.0000044619346,0.000101341044,0.56455135,0.42673108,0.005415437,0.0015354347,0.0001972417],"about_ca_topic_score_codex":0.000030830233,"about_ca_topic_score_gemma":0.000010282808,"teacher_disagreement_score":0.4877087,"about_ca_system_score_codex":0.000028330956,"about_ca_system_score_gemma":0.00011068532,"threshold_uncertainty_score":0.41640073},"labels":[],"label_agreement":null},{"id":"W4417470361","doi":"10.1109/tmm.2025.3645632","title":"StereoMamba+: A Novel Stereo Image Super-Resolution Framework With Adaptive Dependency Capture and Enhanced Feature Fusion","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Telus","keywords":"Stereo image; Epipolar geometry; Block (permutation group theory); Convolutional neural network; Feature extraction; Computational complexity theory; Stereo cameras; Computer stereo vision; Stereopsis; Pattern recognition (psychology)","score_opus":0.011490956799198978,"score_gpt":0.2671166456396222,"score_spread":0.2556256888404232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417470361","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019004733,0.001132398,0.9910396,0.0019044013,0.0013015365,0.0015023905,0.00015886898,0.00072904606,0.00033128483],"genre_scores_gemma":[0.42813808,0.0002759599,0.56969744,0.00037913673,0.000050374896,0.00015886805,0.0000035658811,0.000049041508,0.0012475607],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995415,0.00020886851,0.00062763196,0.0019529619,0.00082379585,0.00097179733],"domain_scores_gemma":[0.9966981,0.000582158,0.00032439575,0.0013331803,0.00071662996,0.0003455349],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0003007897,0.0009955422,0.00075128564,0.00074929255,0.0010313089,0.00058509863,0.0010341045,0.00090471696,0.000050347186],"category_scores_gemma":[0.000063241285,0.0009227488,0.00018830046,0.0017975653,0.0007470797,0.002498194,0.000038259233,0.0026519324,0.000025206999],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023715496,0.0020353699,0.00001577868,0.0005202792,0.00037858353,0.00010166579,0.0121725,0.0011686368,0.24823502,0.00029790122,0.00030576135,0.73239696],"study_design_scores_gemma":[0.00440409,0.0014824381,0.0004304191,0.0059440024,0.00046835266,0.00012852582,0.0009789874,0.694675,0.2869564,0.002525645,0.00020595251,0.0018001698],"about_ca_topic_score_codex":0.00011284962,"about_ca_topic_score_gemma":0.00021517595,"teacher_disagreement_score":0.7305968,"about_ca_system_score_codex":0.00037921587,"about_ca_system_score_gemma":0.00053949334,"threshold_uncertainty_score":0.999649},"labels":[],"label_agreement":null},{"id":"W6888790926","doi":"10.22091/jptr.2024.10366.3014","title":"Should Kane Abandon the Symmetry of Efforts of Will","year":2024,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Luck; Dual (grammatical number); Free will; Worry","score_opus":0.2112866598615777,"score_gpt":0.5580239722461384,"score_spread":0.34673731238456074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6888790926","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.117567666,0.116195135,0.75519633,0.0011671549,0.0013529042,0.00089535397,0.000032257016,0.00041957776,0.007173589],"genre_scores_gemma":[0.9620812,0.0051601413,0.0321705,0.00021458088,0.000089829795,0.00002837702,0.0000017201604,0.00004464133,0.00020901355],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99707156,0.00016652967,0.00099935,0.00049522804,0.00092913414,0.00033819134],"domain_scores_gemma":[0.9970099,0.00061817793,0.0008386209,0.0009945577,0.0004208186,0.00011793054],"candidate_categories":["scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0018221393,0.00029091883,0.0007079895,0.0007859912,0.00014012278,0.0010652435,0.0066065663,0.000100867896,0.00038086867],"category_scores_gemma":[0.0003470449,0.00020419291,0.000231766,0.0021129218,0.00030041576,0.0052652736,0.0022067756,0.000509704,0.000002637673],"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":[0.00012956237,0.0004537497,0.03641289,0.0014507726,0.00042991023,0.00015554595,0.0012270786,0.00016315268,0.5635945,0.013343893,0.044928286,0.33771065],"study_design_scores_gemma":[0.00032216657,0.000043847245,0.0262562,0.0030709843,0.000108778004,0.0000969155,0.000033943357,0.006726313,0.77049565,0.17572226,0.016539125,0.0005838128],"about_ca_topic_score_codex":0.00018275536,"about_ca_topic_score_gemma":0.000005096142,"teacher_disagreement_score":0.84451354,"about_ca_system_score_codex":0.00006154106,"about_ca_system_score_gemma":0.0002061693,"threshold_uncertainty_score":0.99997175},"labels":[],"label_agreement":null},{"id":"W6894355428","doi":"10.5683/sp3/8lts5k","title":"Literature Review Vegetation in Reservoir Management","year":2024,"lang":"en","type":"dataset","venue":"Borealis","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Vegetation (pathology); Hydrology (agriculture); Vegetation classification; Vegetation cover","score_opus":0.014729898411932497,"score_gpt":0.3121771796808152,"score_spread":0.2974472812688827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6894355428","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.4265543e-9,0.15310979,0.012766558,0.0014522317,0.000116158604,0.00040324256,0.83094424,0.00030598557,0.0009017773],"genre_scores_gemma":[2.6330403e-8,0.1360151,0.109759115,0.0013372402,0.000045770536,0.00018418154,0.7525715,0.00001217475,0.00007486711],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983707,0.00007192529,0.00034753885,0.00062713743,0.0003559019,0.00022680272],"domain_scores_gemma":[0.99833435,0.00002396783,0.00013240059,0.0013844094,0.0000822232,0.000042647724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038643213,0.0002535259,0.00028651333,0.00038420933,0.000029118497,0.00034449107,0.0016784664,0.00014373734,0.000001944147],"category_scores_gemma":[0.00006806376,0.00022487808,0.00006585679,0.0010649142,0.000022364875,0.000546643,0.00070941803,0.00051028264,0.00007050226],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.1419856e-7,0.00001553363,6.5379346e-8,0.014058192,0.0000075640414,0.0006410256,0.0000186316,4.778492e-7,3.378277e-7,0.00056264614,0.9753628,0.0093320375],"study_design_scores_gemma":[0.000036094792,0.000013828099,0.0000062804033,0.04331169,0.0000231838,0.000020231699,4.166853e-7,0.00019253418,0.0000048624784,0.016934346,0.9392462,0.00021033366],"about_ca_topic_score_codex":0.0001359639,"about_ca_topic_score_gemma":0.00048047522,"teacher_disagreement_score":0.09699255,"about_ca_system_score_codex":0.00011524829,"about_ca_system_score_gemma":0.000043450993,"threshold_uncertainty_score":0.91702616},"labels":[],"label_agreement":null},{"id":"W6904737977","doi":"10.1371/journal.pone.0259712.g004","title":"Study samples (marked as P1, P2, etc.), their nearest three neighbors, and a random selection of 25 Canadian and 25 global SARS-CoV-2 sequences for context.","year":2021,"lang":"en","type":"other","venue":"Figshare","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Feature selection; Sequence (biology); Random sequence; k-nearest neighbors algorithm","score_opus":0.06204877688954163,"score_gpt":0.3202878245536225,"score_spread":0.25823904766408085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6904737977","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009297873,0.07812435,0.12282048,0.001884592,0.0009228948,0.021808418,0.6574181,0.004255576,0.111835845],"genre_scores_gemma":[0.39380136,0.0005158951,0.5141714,0.004512936,0.0014526512,0.0071203127,0.05927717,0.001541306,0.01760693],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99845093,0.00007072704,0.0002500325,0.0007064654,0.0001890339,0.00033279668],"domain_scores_gemma":[0.998796,0.00017563412,0.00030853137,0.00037524186,0.00024099847,0.00010358727],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010714734,0.000352789,0.0005242253,0.00016612619,0.00015825435,0.00035696177,0.0006083732,0.00021953706,0.0010086799],"category_scores_gemma":[0.0007328857,0.00032050378,0.00006200064,0.00037172434,0.00004787766,0.00025576408,0.00024874398,0.00015696732,0.0000028226586],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007714064,0.00012987654,0.0027306064,0.0015334566,0.00035442936,0.0000819068,0.00082187506,4.7598573e-7,0.00046168012,0.0007400445,0.88929564,0.103772886],"study_design_scores_gemma":[0.004462945,0.00092096435,0.003921934,0.01198867,0.000090106354,0.0002321875,0.00038182683,0.0053510787,0.0027153178,0.013896615,0.9542406,0.0017977112],"about_ca_topic_score_codex":0.025229694,"about_ca_topic_score_gemma":0.40878183,"teacher_disagreement_score":0.5981409,"about_ca_system_score_codex":0.00010612904,"about_ca_system_score_gemma":0.00076894445,"threshold_uncertainty_score":0.9999247},"labels":[],"label_agreement":null},{"id":"W6912522372","doi":"10.5281/zenodo.4179523","title":"-[REGARDER@!! 2Frères - L'improbable parcours (2020) Film Complet Qualité HD STREAMING (VOSTFR) En Francais xia","year":2020,"lang":"fr","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"French; Social life; Electronic mail","score_opus":0.047764924601150255,"score_gpt":0.27805118921947186,"score_spread":0.2302862646183216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6912522372","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012469494,0.0043445653,0.9184237,0.037303627,0.00040555507,0.0009730523,0.00056459435,0.0048365365,0.031901408],"genre_scores_gemma":[0.4360371,0.0035375343,0.5196111,0.00890727,0.0031684095,0.0000012378997,0.0032805044,0.00985296,0.0156038795],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99464244,0.001225221,0.00070042943,0.0013507165,0.00095092866,0.0011302842],"domain_scores_gemma":[0.9965433,0.00014067204,0.00037605036,0.0010594879,0.0012419126,0.00063857454],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0012442186,0.00047437794,0.00048810878,0.00019660358,0.0035475488,0.0033753864,0.0040897042,0.00020094309,0.008189649],"category_scores_gemma":[0.0026924699,0.0005633698,0.00013509383,0.0016796886,0.00069194176,0.002015078,0.005002041,0.0009784454,0.0078855315],"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":[0.00007730469,0.00027846935,0.0000097743105,0.00066962297,0.000095895804,0.00018467008,0.012581781,0.00045902864,0.009109755,0.018267,0.5408832,0.41738352],"study_design_scores_gemma":[0.0005687104,0.0005336179,0.00017821627,0.00022632748,0.000028920942,0.00018927205,0.0005594326,0.06433464,0.0014414709,0.0031170845,0.92823917,0.0005831226],"about_ca_topic_score_codex":0.00012387623,"about_ca_topic_score_gemma":0.0000016697991,"teacher_disagreement_score":0.43479013,"about_ca_system_score_codex":0.00027526452,"about_ca_system_score_gemma":0.000040708772,"threshold_uncertainty_score":0.9996818},"labels":[],"label_agreement":null},{"id":"W6931952111","doi":"10.5683/sp3/uw0kq2","title":"Scene of the Crime","year":2025,"lang":"en","type":"dataset","venue":"Borealis","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary; SAIT Polytechnic","funders":"","keywords":"Crime scene; Agency (philosophy); Action (physics)","score_opus":0.012628423086989249,"score_gpt":0.2920446396630492,"score_spread":0.27941621657606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931952111","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[7.1776234e-9,0.00025365458,0.11648957,0.00028429384,0.00012036738,0.00010813548,0.88160235,0.00010892671,0.0010327019],"genre_scores_gemma":[5.601247e-7,0.000098651006,0.14306809,0.0005974921,0.00003680741,0.000019716115,0.85602665,0.0000036957654,0.00014833291],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990212,0.00004798289,0.00022035056,0.00029316722,0.00026408935,0.00015321372],"domain_scores_gemma":[0.9974151,0.00005447872,0.00023874224,0.0021287994,0.00013951935,0.000023325852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001245754,0.00015571862,0.00021364373,0.000094155526,0.00006760693,0.000058036487,0.0036228811,0.00012136962,0.0000023330238],"category_scores_gemma":[0.0002480026,0.00010824669,0.000084037674,0.00035350097,0.000100843594,0.00014844922,0.0012628948,0.00023008272,3.3397194e-7],"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":[6.701023e-7,0.000017294133,5.0258876e-7,0.00009022662,0.0000060211582,0.0000020364748,0.000005227593,1.5539737e-7,0.000021909062,0.0008247761,0.9951417,0.0038894769],"study_design_scores_gemma":[0.000034628203,0.000009953473,0.00003224739,0.00023038966,0.000014555797,0.0000029226549,4.1357688e-7,0.000115429524,0.0028344316,0.0071046427,0.98951495,0.00010544818],"about_ca_topic_score_codex":0.0029990836,"about_ca_topic_score_gemma":0.00019770926,"teacher_disagreement_score":0.026578525,"about_ca_system_score_codex":0.000035573557,"about_ca_system_score_gemma":0.00027374172,"threshold_uncertainty_score":0.6732275},"labels":[],"label_agreement":null},{"id":"W7017675098","doi":"","title":"Calibration Method for Sparse Multi-view Cameras by Bridging with a Mobile Camera","year":2017,"lang":"en","type":"article","venue":"Institutional Repositories DataBase (IRDB)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bridging (networking); Calibration; Image processing; Camera resectioning; Mode (computer interface); Image (mathematics)","score_opus":0.027550695498623136,"score_gpt":0.3387813179188665,"score_spread":0.31123062242024335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7017675098","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048833067,0.00078281906,0.9961523,0.00045800692,0.0006074626,0.0006266408,0.00028608387,0.0004437824,0.00015462002],"genre_scores_gemma":[0.030032195,0.00005172069,0.9682632,0.0002543582,0.00028200902,0.00060849916,0.00023656944,0.000022775419,0.00024863667],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800295,0.000055325283,0.00036458022,0.00077489926,0.00044891797,0.0003533515],"domain_scores_gemma":[0.9974601,0.00012802107,0.00043548207,0.0014301483,0.00040424394,0.00014200078],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003922498,0.0002785024,0.00027794347,0.0000721293,0.0020456186,0.0011382272,0.0012113686,0.0000675576,0.000002031237],"category_scores_gemma":[0.0005040266,0.00024712455,0.000061295366,0.000148927,0.00037305677,0.005795892,0.00047023516,0.00020053926,0.0000033410597],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005666233,0.0015136113,0.005745018,0.0012891495,0.0004100395,0.0008716038,0.0015345053,0.0024142792,0.40113562,0.26423064,0.039544176,0.28074473],"study_design_scores_gemma":[0.0016317406,0.00030001934,0.000264618,0.0010436352,0.00007372295,0.0006925456,0.000032100914,0.5152394,0.29125968,0.001203244,0.1871227,0.0011366167],"about_ca_topic_score_codex":0.000733879,"about_ca_topic_score_gemma":0.00006181962,"teacher_disagreement_score":0.51282513,"about_ca_system_score_codex":0.00017327197,"about_ca_system_score_gemma":0.000557354,"threshold_uncertainty_score":0.9999981},"labels":[],"label_agreement":null},{"id":"W7019977940","doi":"","title":"Inventory of land use and land use practices in the Canadian Great Lakes Basin: Report of the International Reference Group on Great Lakes Pollution from Land Use Activities, Volume I, Canadian Great Lakes Basin Summary","year":2018,"lang":"en","type":"report","venue":"The Atrium (University of Guelph)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Land use; Structural basin; Recreation; Volume (thermodynamics); Pollution; Drainage basin; Hydrology (agriculture); Baseline (sea)","score_opus":0.05766511662067969,"score_gpt":0.25897497021683274,"score_spread":0.20130985359615305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7019977940","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9875491,0.00045364193,0.00072517287,0.004592115,0.0006082682,0.00077357027,0.0022421097,0.000085518244,0.002970484],"genre_scores_gemma":[0.9905131,0.000929771,0.003726702,0.00019857567,0.0001267873,0.0000023840148,0.00028119143,0.000028530056,0.0041929944],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971365,0.00045899954,0.00039370623,0.0006471885,0.000979359,0.00038425127],"domain_scores_gemma":[0.99560106,0.00056979473,0.0016766561,0.0013837443,0.0005972095,0.00017156616],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012322983,0.0003626916,0.00055463816,0.0006271452,0.0004795709,0.00030015007,0.0020967498,0.000341652,0.000029741706],"category_scores_gemma":[0.0010346142,0.0002737646,0.00013809114,0.0004999493,0.00084628514,0.0026283131,0.00045263147,0.00062282605,0.0000013084644],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019817194,0.00007412545,0.9706167,0.00013912638,0.0002390452,0.0005736957,0.0024055503,0.000013341127,0.00044779974,0.00014246504,0.023710065,0.0014399199],"study_design_scores_gemma":[0.0004579732,0.00016830326,0.8453808,0.0009386845,0.0002301056,0.0003040152,0.00031391505,0.0008396,0.000077998666,0.0005559937,0.15028933,0.00044332127],"about_ca_topic_score_codex":0.9457382,"about_ca_topic_score_gemma":0.98933136,"teacher_disagreement_score":0.12657925,"about_ca_system_score_codex":0.00076829403,"about_ca_system_score_gemma":0.0024500347,"threshold_uncertainty_score":0.99997145},"labels":[],"label_agreement":null},{"id":"W7024164564","doi":"","title":"Quebec hydro project could spoil a pristine Maine wilderness","year":2018,"lang":"en","type":"other","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Wilderness; Wilderness area; Agency (philosophy); Work (physics)","score_opus":0.015129101691170429,"score_gpt":0.2944120470191517,"score_spread":0.27928294532798126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024164564","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.70606e-7,0.00020394096,0.5903158,0.00019499355,0.00016757629,0.0003105624,0.0000032610358,0.0037106494,0.4050928],"genre_scores_gemma":[0.00000825764,0.000025337195,0.4762631,0.00023901205,0.00024502972,0.0000508386,0.000007704113,0.00020504693,0.52295566],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.997805,0.00004549069,0.0002929368,0.0009600656,0.00042996206,0.00046654837],"domain_scores_gemma":[0.9979794,0.00003092573,0.00036172167,0.0014335263,0.000117274416,0.00007714847],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020638602,0.0004787406,0.00045496822,0.000401915,0.000071177376,0.00025008497,0.0021377646,0.00032128583,0.0005369618],"category_scores_gemma":[0.00008239703,0.00039799666,0.00007421076,0.00056095724,0.00024240189,0.000322462,0.00075216894,0.00024965164,0.0003762034],"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":[0.0000031003226,0.00005438929,0.0000074522854,0.00012970055,0.000017717442,0.000035578563,0.000042324704,3.2493052e-8,0.00008825848,0.0046063946,0.9666316,0.028383456],"study_design_scores_gemma":[0.00018560837,0.00006582774,0.0000043912005,0.0004555314,0.000010392709,0.0000418046,0.000002721737,0.0042484435,0.0008427916,0.002711622,0.9908395,0.00059140637],"about_ca_topic_score_codex":0.0041603073,"about_ca_topic_score_gemma":0.0024680006,"teacher_disagreement_score":0.11786286,"about_ca_system_score_codex":0.00011319052,"about_ca_system_score_gemma":0.0004438923,"threshold_uncertainty_score":0.9998472},"labels":[],"label_agreement":null},{"id":"W7027152786","doi":"","title":"Camera-independent learning and image quality assessment for super-resolution","year":2007,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Image quality; Quality (philosophy); Quality assessment; Image (mathematics); Image processing; Feature (linguistics)","score_opus":0.02557063817910054,"score_gpt":0.3524309724688175,"score_spread":0.32686033428971695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027152786","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4377331,0.003676311,0.41054934,0.00029459383,0.005451935,0.008597627,0.0011922743,0.010039725,0.12246508],"genre_scores_gemma":[0.26368326,0.00024973956,0.7304686,0.00018781394,0.000058775575,0.00038207707,0.00053386175,0.00018014328,0.004255748],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99435616,0.00043217716,0.0011521622,0.00188858,0.0011538855,0.0010170313],"domain_scores_gemma":[0.99623626,0.0005377281,0.0009656383,0.00086562894,0.0010533516,0.00034137332],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0032858937,0.0008399405,0.0008423427,0.0005590051,0.002165825,0.0005017608,0.0012800269,0.0007457532,0.000018233166],"category_scores_gemma":[0.0012513819,0.00094151427,0.00028893122,0.00054670824,0.000096140655,0.0027332862,0.00044656408,0.0021263275,0.000018156083],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013135005,0.00022148472,0.00005090069,0.0008894954,0.00009820216,0.000044277152,0.000030948268,0.000010331762,0.24657457,0.14014359,0.000007775782,0.6117971],"study_design_scores_gemma":[0.004141389,0.0014338072,0.005601798,0.0019327877,0.00037686058,0.00017387555,0.0010729516,0.012739803,0.4567277,0.39490408,0.11436874,0.00652621],"about_ca_topic_score_codex":0.00017983699,"about_ca_topic_score_gemma":0.000333417,"teacher_disagreement_score":0.60527086,"about_ca_system_score_codex":0.0011154029,"about_ca_system_score_gemma":0.00014335773,"threshold_uncertainty_score":0.9993035},"labels":[],"label_agreement":null},{"id":"W7036699581","doi":"","title":"Ce que veulent les filles: Une lecture affective du recours aux méthodes d’entrevues réalisées par paire dans le cadre d’une recherche sur les jeunes filles militantes et leurs relations avec leurs mères et figures maternelles","year":2024,"lang":"en","type":"other","venue":"Journals @ The Mount (Mount Saint Vincent University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Context (archaeology); Interview; Field (mathematics); Negotiation; Subject (documents); Subject matter","score_opus":0.0907068402914326,"score_gpt":0.30665579140716354,"score_spread":0.21594895111573092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036699581","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026179273,0.07704893,0.79424053,0.08246194,0.0012207871,0.0022650026,0.00095696875,0.0042774286,0.011349122],"genre_scores_gemma":[0.35592973,0.1823172,0.22860536,0.0021959567,0.0013951687,0.00013913948,0.00038054114,0.0024243053,0.22661263],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9928298,0.0030511953,0.0007655167,0.001531758,0.0009081529,0.00091357384],"domain_scores_gemma":[0.99502206,0.0016090132,0.0012104939,0.0013429632,0.0005639764,0.00025148],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0019868044,0.0012802314,0.0011354146,0.0012783868,0.0015286182,0.00079141336,0.0031357568,0.00091654685,0.00015616707],"category_scores_gemma":[0.0009633503,0.0010083843,0.00064597063,0.0017745702,0.00088020077,0.001227757,0.001435852,0.0024496473,0.000034185425],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034258116,0.0014315471,0.0059980256,0.002409957,0.003055496,0.0031168738,0.072556935,0.0072804946,0.009030973,0.5950516,0.2809518,0.018773766],"study_design_scores_gemma":[0.00096290396,0.00034752264,0.0019819948,0.014361812,0.0006394848,0.0004733073,0.032491013,0.006553109,0.005083517,0.06503883,0.8693927,0.00267379],"about_ca_topic_score_codex":0.040358134,"about_ca_topic_score_gemma":0.024519274,"teacher_disagreement_score":0.58844095,"about_ca_system_score_codex":0.0067304587,"about_ca_system_score_gemma":0.00093586475,"threshold_uncertainty_score":0.99999493},"labels":[],"label_agreement":null},{"id":"W7036893204","doi":"","title":"Contribució al catàleg espeològic de l'Urgell","year":2022,"lang":"ca","type":"article","venue":"Dialnet (Universidad de la Rioja)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Quarter (Canadian coin); Period (music); Subject (documents)","score_opus":0.008054697994499087,"score_gpt":0.2643446600911889,"score_spread":0.2562899620966898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036893204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01769976,0.0030315258,0.9313717,0.008521957,0.00077194703,0.0005751514,0.00017015275,0.0014149086,0.036442883],"genre_scores_gemma":[0.84484035,0.00035334262,0.14513025,0.00483806,0.00017330419,0.000048135415,0.00003489211,0.000096584256,0.0044851075],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9951816,0.0014835029,0.00033327512,0.0010393031,0.0007360154,0.0012262694],"domain_scores_gemma":[0.99698734,0.0009527169,0.00041099533,0.0010549454,0.00016628022,0.00042770724],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018109928,0.00047125935,0.0005193632,0.0005606237,0.0011946617,0.00060199667,0.0027657934,0.000248549,0.0005773673],"category_scores_gemma":[0.00026741085,0.00064288167,0.0002682153,0.0016034538,0.00055800093,0.0011888954,0.0022229115,0.0014680648,0.00007821173],"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":[0.00054921466,0.0018658574,0.005127243,0.00029916936,0.00043234442,0.0152364345,0.03240127,0.0025942707,0.037786845,0.3161407,0.41915274,0.16841389],"study_design_scores_gemma":[0.0021791756,0.00041792504,0.002406256,0.000090783826,0.00012397731,0.00089236157,0.0010040762,0.110198036,0.0025513237,0.048161797,0.8307526,0.0012216906],"about_ca_topic_score_codex":0.00033851466,"about_ca_topic_score_gemma":0.00001645166,"teacher_disagreement_score":0.82714057,"about_ca_system_score_codex":0.0015377998,"about_ca_system_score_gemma":0.0016473687,"threshold_uncertainty_score":0.99960226},"labels":[],"label_agreement":null},{"id":"W7039684745","doi":"","title":"âNecessary Stepping Stonesâ: The Transfer of &lt;em&gt;Aurora&lt;/em&gt;, &lt;em&gt;Patriot&lt;/em&gt;, and &lt;em&gt;Patrician&lt;/em&gt; to the Royal Canadian Navy after the First World War","year":2012,"lang":"en","type":"article","venue":"Scholars Commons (Wilfrid Laurier University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Navy; First world war; Project commissioning; World War II; Spring (device); Spanish Civil War; Maritime history","score_opus":0.011090634684953976,"score_gpt":0.21454241025065274,"score_spread":0.20345177556569877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039684745","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78821504,0.027606787,0.13416243,0.018134763,0.0039316537,0.007431022,0.0016856927,0.0028191016,0.016013483],"genre_scores_gemma":[0.9702433,0.00055106974,0.019491836,0.0035549907,0.0006201379,0.00012337528,0.0000700353,0.00035516318,0.0049900888],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9869963,0.0017916174,0.0017627305,0.0027439974,0.0028710312,0.0038343095],"domain_scores_gemma":[0.98894334,0.001269894,0.0008716834,0.005481474,0.0014688214,0.0019648154],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0041708094,0.0021683285,0.0018994877,0.0031270997,0.004998021,0.002003975,0.010248694,0.0008774871,0.0002388503],"category_scores_gemma":[0.0005744431,0.0017810517,0.0009328564,0.008322799,0.0012865134,0.006421003,0.0035669398,0.0035321058,0.00017642413],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0070851888,0.0067183194,0.06978228,0.0029070764,0.0067792335,0.00786512,0.20301098,0.007909027,0.021137873,0.100793764,0.21074025,0.3552709],"study_design_scores_gemma":[0.0028637776,0.00056823454,0.023682242,0.0010029117,0.0009592691,0.00020813421,0.0028049804,0.0050341995,0.0020817113,0.0011579508,0.9560739,0.0035627305],"about_ca_topic_score_codex":0.0012796453,"about_ca_topic_score_gemma":0.17226698,"teacher_disagreement_score":0.7453336,"about_ca_system_score_codex":0.0015381697,"about_ca_system_score_gemma":0.0014386995,"threshold_uncertainty_score":0.99910575},"labels":[],"label_agreement":null},{"id":"W7066928522","doi":"","title":"It’s not, “let’s do more”. It’s, “let’s do different”: Recognizing the Important Associations between Children’s Reading Skill and their Motivation and Engagement in the Elementary School Classroom","year":2021,"lang":"","type":"dissertation","venue":"TSpace","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Reading motivation; Reading (process); Context (archaeology); Construct (python library); Goal theory; Student engagement; Self-determination theory","score_opus":0.03306950648934034,"score_gpt":0.33236324630806685,"score_spread":0.29929373981872653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7066928522","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8120248,0.0043339,0.12150821,0.055411007,0.00068473804,0.0047315108,0.00023124334,0.00037103414,0.0007035563],"genre_scores_gemma":[0.9629176,0.0062304554,0.023705006,0.0043693963,0.0004407684,0.00046331383,0.0012973897,0.00012967597,0.0004463693],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99272054,0.0014496646,0.0017029598,0.0019288809,0.0011628439,0.001035132],"domain_scores_gemma":[0.9934814,0.002467057,0.0020086814,0.0013941013,0.00044154172,0.00020723854],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0039122473,0.0011429654,0.0010455496,0.00050388655,0.0020590227,0.002493985,0.0017383908,0.0004461812,0.000037799073],"category_scores_gemma":[0.0020256555,0.0008079732,0.0002044648,0.0012203313,0.0002201714,0.0013714101,0.00092982687,0.002680496,0.0000026621701],"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":[0.000071726914,0.00055812125,0.4504021,0.00040222573,0.0009309514,0.00004043743,0.4035231,0.00002009753,0.005562125,0.0017738893,0.004816495,0.13189873],"study_design_scores_gemma":[0.0014711504,0.0002700126,0.8633434,0.0033176371,0.00051125616,0.000039770515,0.107557066,0.004294888,0.010070082,0.0057188226,0.0014190768,0.0019868284],"about_ca_topic_score_codex":0.0004146926,"about_ca_topic_score_gemma":0.00038438555,"teacher_disagreement_score":0.41294128,"about_ca_system_score_codex":0.0005219244,"about_ca_system_score_gemma":0.00035040238,"threshold_uncertainty_score":0.9996204},"labels":[],"label_agreement":null},{"id":"W7115560320","doi":"10.1051/itmconf/20258001003","title":"From Early Models to Modern Techniques: A Deep Learning Survey on Single Image Super-Resolution","year":2025,"lang":"en","type":"article","venue":"ITM Web of Conferences","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Deep learning; Convolutional neural network; Transformer; Key (lock); Artificial neural network; Pattern recognition (psychology); Task (project management); High resolution","score_opus":0.04124198497691799,"score_gpt":0.29713853870260476,"score_spread":0.25589655372568676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115560320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022692172,0.00014970561,0.9639465,0.00036798028,0.00008180353,0.00023623992,0.000011949203,0.0007768325,0.011736814],"genre_scores_gemma":[0.65411556,0.0000134993215,0.3455797,0.00010609136,0.000013803379,0.00002977303,0.000006276099,0.000007933099,0.00012732648],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981802,0.00019825221,0.0003692658,0.0005941076,0.00035286986,0.00030531187],"domain_scores_gemma":[0.9984056,0.00033393266,0.00014969008,0.0005451264,0.0004942896,0.000071367336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043317676,0.00022311702,0.00033274648,0.00035221124,0.00012508074,0.00028360455,0.0012660415,0.000112845395,0.0000079253205],"category_scores_gemma":[0.00028506163,0.00021655917,0.000056764682,0.0005782908,0.00012440728,0.0009420364,0.00037853993,0.00025570308,0.000008044032],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001660762,0.00042238235,0.0029364815,0.000054998636,0.000058126847,0.000010486224,0.0031564184,0.0010638809,0.37125668,0.040988542,0.00087544386,0.5790105],"study_design_scores_gemma":[0.0001832685,0.00049315946,0.002367104,0.0005242095,0.000009250659,5.179879e-7,0.000055971595,0.52989,0.22164515,0.24375261,0.00069733174,0.0003813937],"about_ca_topic_score_codex":0.001609941,"about_ca_topic_score_gemma":0.00023740616,"teacher_disagreement_score":0.6314234,"about_ca_system_score_codex":0.00006470529,"about_ca_system_score_gemma":0.00041317605,"threshold_uncertainty_score":0.88310266},"labels":[],"label_agreement":null},{"id":"W7117736062","doi":"10.18280/ts.420604","title":"Uniform Blur Estimation via Modified PSO and Total Variation-Based Adaptive Regularization for Blind Image Restoration","year":2025,"lang":"","type":"article","venue":"Traitement du signal","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Image restoration; Regularization (linguistics); Image (mathematics); Pattern recognition (psychology); Blind deconvolution; Image denoising","score_opus":0.017720729462017592,"score_gpt":0.27822142793649396,"score_spread":0.2605006984744764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117736062","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00094415067,0.00012441742,0.9938912,0.0019300748,0.00024866057,0.00229147,0.000038927727,0.00032448737,0.00020665566],"genre_scores_gemma":[0.46288735,0.0000041746216,0.5363728,0.0001881528,0.0000613771,0.00023186019,0.00009680908,0.000017867556,0.00013964032],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971198,0.00014993564,0.0008921901,0.0009122733,0.00050434616,0.0004214567],"domain_scores_gemma":[0.9976194,0.00028082964,0.0006243854,0.00042353268,0.00094608177,0.000105790044],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00094903854,0.0004256189,0.0003505308,0.00053000107,0.0007378765,0.0007804841,0.00040820526,0.0002163263,0.000017575221],"category_scores_gemma":[0.00018672139,0.00048659788,0.000091921676,0.00091244,0.00019948946,0.0027211444,0.0001310023,0.0002046758,0.0000024833369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002709896,0.0012096143,0.000034765573,0.0010988377,0.00022126726,0.000007898675,0.003454832,0.15032539,0.14901315,0.2227493,0.0006421508,0.4685329],"study_design_scores_gemma":[0.0027618252,0.0005856019,0.00057158555,0.0002830433,0.00012440287,0.0000025019267,0.000020477533,0.8887258,0.019291542,0.087219454,0.000030253203,0.00038350635],"about_ca_topic_score_codex":0.000019327044,"about_ca_topic_score_gemma":0.0000055368296,"teacher_disagreement_score":0.7384004,"about_ca_system_score_codex":0.00040623551,"about_ca_system_score_gemma":0.0006056879,"threshold_uncertainty_score":0.99975854},"labels":[],"label_agreement":null},{"id":"W7129358542","doi":"10.1109/icipw68931.2025.11386224","title":"Learning Single-Image Super-Resolution In The Jpeg Compressed Domain","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Speedup; JPEG; Pipeline (software); Discrete cosine transform; Overhead (engineering); Quantization (signal processing); Transform coding; Bottleneck; Decoding methods; Deep learning","score_opus":0.01636139269561146,"score_gpt":0.28743933217585904,"score_spread":0.2710779394802476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7129358542","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019404858,0.0010983415,0.9382483,0.009788518,0.00027979878,0.00057665585,8.6004934e-7,0.0006447582,0.0474223],"genre_scores_gemma":[0.48476452,0.00004941579,0.5121683,0.0013927183,0.000038841546,0.000048513582,0.0000025015122,0.000015033198,0.0015201862],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958863,0.00095506397,0.0007572319,0.0009934371,0.00055881176,0.00084911805],"domain_scores_gemma":[0.9977523,0.0006011214,0.0001975607,0.001132598,0.00025628626,0.000060154103],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001670543,0.0004245045,0.00040350732,0.0004656845,0.00072148454,0.0013777129,0.00285221,0.00019217799,0.000046438763],"category_scores_gemma":[0.0005097963,0.0003461385,0.00012404384,0.0025067688,0.0005145094,0.0019227664,0.0011060016,0.0011740061,0.000040322448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015853572,0.0022472576,0.0014401759,0.0006303213,0.000058311663,0.00026943936,0.021348726,0.0016421477,0.3806403,0.1635773,0.010126658,0.41786084],"study_design_scores_gemma":[0.0010870622,0.0003914405,0.0010021938,0.0009325143,0.00002313424,0.000032378837,0.0017506871,0.8359707,0.021876574,0.10717135,0.029056815,0.00070513843],"about_ca_topic_score_codex":0.000105538034,"about_ca_topic_score_gemma":0.000050986233,"teacher_disagreement_score":0.8343286,"about_ca_system_score_codex":0.00026831578,"about_ca_system_score_gemma":0.00023296493,"threshold_uncertainty_score":0.9998991},"labels":[],"label_agreement":null},{"id":"W7131114666","doi":"10.1109/iccvw69036.2025.00592","title":"RCENet: Recursive Concatenation and Enhancement Network for Real-Time Super-Resolution","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BlueDot (Canada)","funders":"","keywords":"Concatenation (mathematics); Inference; Enhanced Data Rates for GSM Evolution; Edge device; Quantization (signal processing); Edge computing; Latency (audio); Architecture","score_opus":0.011833200948337586,"score_gpt":0.2935323564995894,"score_spread":0.28169915555125186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131114666","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036000623,0.0020839565,0.98549134,0.0042924257,0.00051641115,0.0013328566,0.0000068492045,0.00036483747,0.005551319],"genre_scores_gemma":[0.016834453,0.001902095,0.96707577,0.00071398885,0.00012472314,0.00023967319,0.000022373679,0.000018827453,0.013068093],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974837,0.00010887718,0.00058662775,0.00095071725,0.00022732781,0.0006428016],"domain_scores_gemma":[0.99822384,0.00026241425,0.00020645677,0.00051184813,0.00070147536,0.00009398169],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007839327,0.00032084342,0.000363445,0.00015700764,0.0005828571,0.000378678,0.0005422038,0.00017865527,0.000034897654],"category_scores_gemma":[0.00018384952,0.00033344526,0.000068391266,0.00066667347,0.00022906465,0.0010394729,0.0004618271,0.00013743169,0.000011567889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020581104,0.000208309,0.00010932769,0.00037373454,0.000106295025,0.0000026425378,0.00084002485,0.0003047054,0.04939361,0.22525577,0.051103786,0.672096],"study_design_scores_gemma":[0.0007070996,0.00045157108,0.00019853961,0.0005629918,0.000057490965,0.0000030077774,0.000024754807,0.8196814,0.034820408,0.13314877,0.009933485,0.00041044573],"about_ca_topic_score_codex":0.000052172938,"about_ca_topic_score_gemma":0.000012313786,"teacher_disagreement_score":0.8193767,"about_ca_system_score_codex":0.00029448417,"about_ca_system_score_gemma":0.00030620754,"threshold_uncertainty_score":0.9999118},"labels":[],"label_agreement":null},{"id":"W7131118221","doi":"10.1109/iccvw69036.2025.00318","title":"Efficient Depth- and Spatially-Varying Image Simulation for Defocus Deblur","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Autofocus; Domain (mathematical analysis); Image (mathematics); Face (sociological concept); Deep learning; Image resolution; Computational complexity theory; Resolution (logic); Scalability","score_opus":0.01679200898032822,"score_gpt":0.32553201285017913,"score_spread":0.3087400038698509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131118221","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011255138,0.0014227494,0.99012655,0.001016663,0.0003696734,0.0012875916,0.0000032553098,0.00061595306,0.004032066],"genre_scores_gemma":[0.46322805,0.000020372536,0.53596705,0.00032596107,0.000029228175,0.000053588792,0.0000010748896,0.00001463262,0.0003600563],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974279,0.00006539765,0.00063223194,0.001035897,0.00025801302,0.0005805924],"domain_scores_gemma":[0.9975116,0.0009201244,0.0002388713,0.00064968056,0.0005660431,0.0001136731],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005004285,0.00037074866,0.00035048684,0.00033014856,0.0006249477,0.00082001893,0.0006676987,0.00015156184,0.000009979764],"category_scores_gemma":[0.0009781073,0.00037271428,0.00010547945,0.0006962618,0.0002064107,0.00062271167,0.0008173929,0.00018436888,0.0000054992606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013709917,0.00018645827,0.0001829141,0.0006670039,0.000036578833,0.0000071770246,0.00058206194,0.06969143,0.014232602,0.015667148,0.000111361886,0.8984982],"study_design_scores_gemma":[0.00104916,0.0001065721,0.00017665468,0.0003268485,0.00004253134,0.0000034626044,0.0000088636625,0.94581294,0.029987333,0.021721778,0.00040223973,0.00036159242],"about_ca_topic_score_codex":0.000030361745,"about_ca_topic_score_gemma":0.000023206872,"teacher_disagreement_score":0.89813656,"about_ca_system_score_codex":0.00014197965,"about_ca_system_score_gemma":0.00034588974,"threshold_uncertainty_score":0.9998725},"labels":[],"label_agreement":null},{"id":"W7160064712","doi":"10.1109/iccv51701.2025.02452","title":"Splat-Based 3D Scene Reconstruction with Extreme Motion-Blur","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hyperion Technologies (Canada); Kootenay Association for Science & Technology","funders":"Microsoft Research Asia; Samsung","keywords":"Object (grammar); Feature (linguistics); Perspective (graphical); Field (mathematics); Noise (video)","score_opus":0.018522089906127116,"score_gpt":0.25786506266598125,"score_spread":0.23934297275985414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160064712","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026915618,0.0005662115,0.97369933,0.0027009544,0.000715489,0.0004389908,0.0000022460283,0.0013193332,0.0202883],"genre_scores_gemma":[0.1711951,0.000032582426,0.82459575,0.00089624914,0.00005308137,0.000036670717,0.0000021678504,0.00002120182,0.003167208],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99706674,0.00010573655,0.0005953948,0.0012237518,0.00042092064,0.0005874607],"domain_scores_gemma":[0.99750173,0.00011404853,0.0003162502,0.0012451744,0.00069133873,0.00013145097],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037409793,0.00045435957,0.0003867791,0.0005501923,0.00051566033,0.0006449984,0.0010299028,0.00018989577,0.00021825077],"category_scores_gemma":[0.00012040337,0.0004127033,0.00008479045,0.0020711825,0.00046141772,0.001974859,0.00026333352,0.00042712037,0.00004050769],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006557741,0.00015964944,0.0019594217,0.00017252828,0.000029478235,0.000017875514,0.00005422582,0.00026737695,0.0033401744,0.0064782994,0.00036433453,0.98709106],"study_design_scores_gemma":[0.00086066936,0.00019852734,0.0004677293,0.0012402928,0.00004658577,0.00005720107,0.000020546515,0.9112893,0.07101063,0.013094014,0.0011722718,0.0005422883],"about_ca_topic_score_codex":0.000046976027,"about_ca_topic_score_gemma":0.00002878302,"teacher_disagreement_score":0.9865488,"about_ca_system_score_codex":0.00028208815,"about_ca_system_score_gemma":0.0009083055,"threshold_uncertainty_score":0.99983245},"labels":[],"label_agreement":null},{"id":"W7160064931","doi":"10.1109/iccv51701.2025.01147","title":"ZFusion: Efficient Deep Compositional Zero-Shot Learning for Blind Image Super-Resolution with Generative Diffusion Prior","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Pattern recognition (psychology); Image (mathematics); Deep learning; Image processing; Generative model","score_opus":0.016570467499124237,"score_gpt":0.2996730104554336,"score_spread":0.2831025429563094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160064931","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011387062,0.00083530793,0.97966945,0.003113582,0.0003772756,0.0018975219,0.0000084104195,0.0006633739,0.0020479911],"genre_scores_gemma":[0.2615373,0.000044881668,0.7353077,0.00057859963,0.00009407712,0.00022634365,0.000047013484,0.00003367674,0.002130401],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957006,0.00024773795,0.00078152295,0.0016113331,0.0007759483,0.00088285794],"domain_scores_gemma":[0.99678165,0.0004166996,0.0003588489,0.00067195244,0.001577238,0.00019362848],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00061850424,0.00064934866,0.00056437444,0.00050827925,0.0026139165,0.0009567306,0.0010222024,0.00023524114,0.00008437244],"category_scores_gemma":[0.00019607716,0.00055962795,0.00018771572,0.0013152815,0.000587249,0.0009934151,0.0011173045,0.00065756263,0.00001608599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00248114,0.0023504768,0.00023837124,0.0005930928,0.00015856273,0.000048688107,0.003434824,0.0421164,0.7437217,0.052511856,0.0015838606,0.15076101],"study_design_scores_gemma":[0.0026626461,0.00088002405,0.0002321437,0.0006502237,0.00007390528,0.000034444507,0.00010782936,0.9022719,0.08777805,0.0034428905,0.0012445904,0.00062135933],"about_ca_topic_score_codex":0.00001593311,"about_ca_topic_score_gemma":0.0000133305275,"teacher_disagreement_score":0.8601555,"about_ca_system_score_codex":0.00046081762,"about_ca_system_score_gemma":0.00062484026,"threshold_uncertainty_score":0.9996855},"labels":[],"label_agreement":null},{"id":"W7163147446","doi":"10.1109/icsm64417.2025.11541549","title":"Temporal Pyramid Structure for Video Frame Interpolation","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ross Video (Canada); University of Ottawa","funders":"","keywords":"Interpolation (computer graphics); Frame (networking); Pyramid (geometry); Bilinear interpolation; Motion interpolation; Nearest-neighbor interpolation; Data compression; Pattern recognition (psychology)","score_opus":0.010827305278308608,"score_gpt":0.3161451510666069,"score_spread":0.3053178457882983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7163147446","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002219407,0.0007116073,0.9901359,0.0038047526,0.0012231846,0.0008689763,0.000019689664,0.0008784292,0.0021354938],"genre_scores_gemma":[0.39146706,0.000005468228,0.604328,0.0021990158,0.000068693684,0.0000317308,0.0000060148295,0.000014792753,0.0018792795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976406,0.000048412847,0.0006269609,0.0009444476,0.00023466822,0.00050492166],"domain_scores_gemma":[0.99799305,0.00018064222,0.00029116965,0.0009258567,0.0005252146,0.00008405561],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024732595,0.00037363,0.00036301417,0.0003433588,0.0003479455,0.0008321625,0.001456306,0.0002334669,0.000084013715],"category_scores_gemma":[0.00044177368,0.00036114926,0.0001434964,0.00091285584,0.00021680215,0.0018851222,0.00071632594,0.0003850124,0.000005990287],"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":[0.00016491229,0.00016804387,0.0021206972,0.0008591917,0.00010531733,0.0000051329394,0.0009956984,0.000045043642,0.047747124,0.35665986,0.027466383,0.5636626],"study_design_scores_gemma":[0.0002593993,0.000110720284,0.00007782651,0.00024722735,0.00001651864,0.0000031718691,0.000024512574,0.56202763,0.020267446,0.4019516,0.014757824,0.00025609057],"about_ca_topic_score_codex":0.000026686768,"about_ca_topic_score_gemma":0.000021884855,"teacher_disagreement_score":0.56340647,"about_ca_system_score_codex":0.00015295633,"about_ca_system_score_gemma":0.00042395014,"threshold_uncertainty_score":0.99988407},"labels":[],"label_agreement":null},{"id":"W96502290","doi":"","title":"Multi-Frame Super-Resolution with No Explicit Motion Estimation.","year":2008,"lang":"en","type":"article","venue":"IPCV","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Noise reduction; Motion estimation; Frame (networking); Artificial intelligence; Computer vision; Computer science; Filter (signal processing); Noise (video); Algorithm; Resolution (logic); Motion (physics); Image (mathematics); Inter frame; Image denoising; Superresolution; Mathematics; Reference frame","score_opus":0.02528929272835758,"score_gpt":0.265113834098023,"score_spread":0.23982454136966544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W96502290","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004151218,0.00006797977,0.99406946,0.00031761324,0.00006176292,0.00012979929,7.4475145e-7,0.0009526671,0.0002487822],"genre_scores_gemma":[0.24622425,0.000007773931,0.7532131,0.0001487391,0.00002368278,0.00003053918,0.000002838035,0.000009159852,0.00033991947],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913484,0.000021781298,0.0001322373,0.00030058448,0.00021555489,0.00019498768],"domain_scores_gemma":[0.999259,0.000023191418,0.00006555775,0.00041788624,0.00018383533,0.000050525785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007662997,0.00011409278,0.0000960134,0.000078249475,0.00019799672,0.00005826865,0.00039542688,0.000046673442,0.0000061349792],"category_scores_gemma":[0.00008519537,0.00009965376,0.000020638525,0.000269825,0.000060832896,0.0013871636,0.00009275855,0.00011179609,0.00012367612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016836973,0.0023757082,0.013316722,0.00035182558,0.000089204346,0.00052176544,0.012935946,0.020480005,0.29577783,0.035580747,0.021579133,0.59682274],"study_design_scores_gemma":[0.00027274314,0.000089524285,0.002866009,0.000041062896,0.0000022480406,0.00011079115,0.0000038613844,0.98497105,0.009046823,0.0012284373,0.001179715,0.00018772947],"about_ca_topic_score_codex":0.000023009268,"about_ca_topic_score_gemma":0.0000020556154,"teacher_disagreement_score":0.96449107,"about_ca_system_score_codex":0.000063563224,"about_ca_system_score_gemma":0.000042892334,"threshold_uncertainty_score":0.40637624},"labels":[],"label_agreement":null}]}