{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":8,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":8,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"7e39c9b3d563","filters":{"venue":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)"}},"results":[{"id":"W3192778027","doi":"10.1109/wacv51458.2022.00016","title":"Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Deblurring; Computer science; Artificial intelligence; Computer vision; Task (project management); Autofocus; Pixel; Image (mathematics); Image quality; Reflection (computer programming); Image restoration; Image processing; Focus (optics); Optics","retraction":null,"screen_n_in":null,"score":{"opus":0.02019366797951767,"gpt":0.2666047463247991,"spread":0.2464110783452815,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002780747,0.0004346433,0.0004348738,0.0003030013,0.0006200285,0.0003361692,0.0009206897,0.0001030082,0.0001643255],"category_scores_gemma":[0.00001496288,0.0004790927,0.0001992857,0.0006419616,0.0001639816,0.0004240458,0.0005385526,0.0008875582,0.00005512764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001852822,"about_ca_system_score_gemma":0.00005717572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001998525,"about_ca_topic_score_gemma":0.000002833319,"domain_scores_codex":[0.9976293,0.00009856336,0.0005880103,0.0007356771,0.0004734814,0.0004749228],"domain_scores_gemma":[0.9982799,0.0001121198,0.0003021664,0.0009162496,0.0002712438,0.0001183262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003025955,0.0008091977,0.00003734732,0.0002639671,0.00007650766,0.000008551448,0.0009716034,0.008507903,0.7626477,0.002220832,0.01225675,0.2121693],"study_design_scores_gemma":[0.001111741,0.0007618935,0.0000940627,0.000223551,0.0000831269,0.00008287806,0.0008122887,0.7505637,0.1678596,0.001757204,0.07541031,0.00123971],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005976195,0.000193603,0.9887922,0.0009153389,0.0001987606,0.0008417296,0.00008390992,0.001123581,0.001874701],"genre_scores_gemma":[0.8410049,0.00005219159,0.1566572,0.000147936,0.0001475572,0.001213883,0.00008942872,0.0001008848,0.0005860873],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8350287,"threshold_uncertainty_score":0.9997661,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4212935147","doi":"10.1109/wacv51458.2022.00382","title":"Self-Supervised Shape Alignment for Sports Field Registration","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Association des Radiologistes du Québec","funders":"University of British Columbia","keywords":"Homography; Computer science; Artificial intelligence; Pairwise comparison; Image registration; Field (mathematics); Process (computing); Computer vision; Transformation (genetics); Enhanced Data Rates for GSM Evolution; Image (mathematics); Pattern recognition (psychology); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0188087235784151,"gpt":0.2942318517673893,"spread":0.2754231281889742,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004420457,0.0002796728,0.0003374565,0.0002955117,0.0004481006,0.0001665085,0.001672686,0.00005648222,0.0004043308],"category_scores_gemma":[0.000009046887,0.0002909951,0.0002029348,0.0005122708,0.0000424305,0.0004188955,0.0005688948,0.0002791517,0.00002465505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001067336,"about_ca_system_score_gemma":0.0001289515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004330943,"about_ca_topic_score_gemma":0.000001078281,"domain_scores_codex":[0.9972037,0.00009677904,0.0007028211,0.0008910244,0.000768988,0.0003366637],"domain_scores_gemma":[0.9976471,0.0002102721,0.0003999167,0.001336126,0.0002620022,0.0001445654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001068235,0.001020638,0.00006681197,0.00008101953,0.00004497352,0.000006828787,0.0009741788,0.0008290532,0.006034225,0.1137751,0.03193883,0.8451216],"study_design_scores_gemma":[0.0008518136,0.001039972,0.0001100778,0.00005584965,0.00001425619,0.00001958396,0.0001322679,0.8879394,0.004921688,0.008144163,0.0963962,0.0003747113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001488454,0.00003612556,0.9889787,0.005342433,0.0005589029,0.001302238,0.00002800415,0.0002574176,0.002007752],"genre_scores_gemma":[0.6708227,0.00004235439,0.3243441,0.002940205,0.0001619355,0.001063224,0.00005917483,0.0000288329,0.0005375238],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8871104,"threshold_uncertainty_score":0.9999542,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4212898032","doi":"10.1109/wacv51458.2022.00291","title":"One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detection","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Computer science; Adversarial system; Novelty; Encoder; Masking (illustration); Class (philosophy); Context (archaeology); Novelty detection; Artificial intelligence; Decoding methods; Speech recognition; Algorithm; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.02832161546985262,"gpt":0.2784198623457384,"spread":0.2500982468758858,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005184903,0.0003380661,0.0004502978,0.0003222937,0.0009702022,0.0002155819,0.001669746,0.0001170361,0.0001581255],"category_scores_gemma":[0.000006008756,0.0003568094,0.0002361112,0.001014083,0.0001319494,0.0003450251,0.0005629986,0.0005131441,0.00003304301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001876701,"about_ca_system_score_gemma":0.0001644695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003669945,"about_ca_topic_score_gemma":0.00004853985,"domain_scores_codex":[0.9969881,0.0001472701,0.0007079299,0.001086154,0.0006177296,0.0004528767],"domain_scores_gemma":[0.9971976,0.0002488639,0.0006084088,0.001371959,0.000424782,0.0001484412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005184294,0.001053861,0.00003917985,0.00005514821,0.0001678395,0.00000171645,0.0006180536,0.01781505,0.009147353,0.1924194,0.007651036,0.7705129],"study_design_scores_gemma":[0.002273584,0.003632669,0.0003041386,0.0001029289,0.00006780196,0.00004364492,0.0002416075,0.8036603,0.01738347,0.02098144,0.1503949,0.0009134957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001673924,0.00002076837,0.9922608,0.002060222,0.0003843858,0.001995164,0.00005435702,0.0004378691,0.001112483],"genre_scores_gemma":[0.9090461,0.00001387121,0.08605878,0.0008416356,0.0003207053,0.003197115,0.00003973378,0.00004181751,0.000440302],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9073721,"threshold_uncertainty_score":0.9998884,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3183877130","doi":"10.1109/wacv51458.2022.00403","title":"Detail Preserving Residual Feature Pyramid Modules for Optical Flow","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Upsampling; Pyramid (geometry); Feature (linguistics); Computer science; Residual; Modular design; Artificial intelligence; Optical flow; Feature extraction; Iterative method; Flow (mathematics); Computer vision; Pattern recognition (psychology); Algorithm; Image (mathematics); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02115698958245709,"gpt":0.2993427656259109,"spread":0.2781857760434538,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004866532,0.0003592482,0.0004566386,0.0003923332,0.0006364604,0.0002896456,0.003012084,0.00008498672,0.0002029424],"category_scores_gemma":[0.00002735375,0.0003571164,0.0002372802,0.0006675606,0.0001402737,0.0005575294,0.001676949,0.0005974768,0.00004275325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001057961,"about_ca_system_score_gemma":0.0001262821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003642514,"about_ca_topic_score_gemma":0.000003143031,"domain_scores_codex":[0.9968179,0.0001297825,0.0006138415,0.001122564,0.0008137642,0.0005021553],"domain_scores_gemma":[0.9970064,0.0003386046,0.0003121955,0.001739407,0.0003965503,0.0002068973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001617718,0.0007945161,0.00003553498,0.0000866238,0.00006623618,0.000008002982,0.0006744745,0.01217574,0.01188168,0.1199642,0.0757661,0.7783851],"study_design_scores_gemma":[0.0009094944,0.000724554,0.0002801127,0.00008113041,0.00001571883,0.00002673443,0.0001256441,0.9152645,0.006227034,0.01328035,0.06262325,0.0004415105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001363601,0.00009378947,0.9873847,0.008016948,0.0005393171,0.001098378,0.00008612893,0.0002578197,0.001159284],"genre_scores_gemma":[0.3404148,0.00002858043,0.6549609,0.001511379,0.0003029215,0.001081482,0.00008870946,0.00005219711,0.001559032],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9030887,"threshold_uncertainty_score":0.9998881,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4213125754","doi":"10.1109/wacv51458.2022.00320","title":"Tailor Me: An Editing Network for Fashion Attribute Shape Manipulation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Image editing; Computer science; Parsing; RGB color model; Artificial intelligence; Video editing; Pixel; Image (mathematics); Computer vision; Task (project management); Computer graphics (images)","retraction":null,"screen_n_in":null,"score":{"opus":0.03525840182600259,"gpt":0.2884128643373655,"spread":0.2531544625113629,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008140722,0.0003230028,0.0004201868,0.0002298001,0.0008567219,0.0002876958,0.001757611,0.00008226188,0.0002979075],"category_scores_gemma":[0.00001194509,0.000337253,0.0002188728,0.0007276766,0.00006931699,0.0006564361,0.0006427002,0.0003164896,0.00003010546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001070006,"about_ca_system_score_gemma":0.00008633608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008349212,"about_ca_topic_score_gemma":0.000009845999,"domain_scores_codex":[0.996947,0.0003135196,0.0007093406,0.0009789539,0.0005858711,0.0004653489],"domain_scores_gemma":[0.9975103,0.000250052,0.0005198575,0.001131635,0.0004287328,0.0001594706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001605015,0.001023607,0.0003484079,0.0000625953,0.0001450231,0.000004757748,0.0009688334,0.2304661,0.01276795,0.1254786,0.06897154,0.5596021],"study_design_scores_gemma":[0.0005471318,0.0009966077,0.0005875349,0.00003767879,0.00002340935,0.000007448698,0.0001280351,0.9765975,0.001056226,0.004034508,0.01562805,0.0003558508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004275312,0.00002691951,0.9907629,0.002030316,0.001174647,0.001250115,0.0000788393,0.0001667954,0.0002341914],"genre_scores_gemma":[0.8823728,0.00001007775,0.1144044,0.0006935396,0.001234666,0.0008472623,0.0002384365,0.00003220377,0.0001666816],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8780975,"threshold_uncertainty_score":0.999908,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3200321642","doi":"10.1109/wacv51458.2022.00101","title":"Auto White-Balance Correction for Mixed-Illuminant Scenes","year":2022,"lang":"en","type":"preprint","venue":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Standard illuminant; Color balance; Computer science; Artificial intelligence; Computer vision; Weighting; Set (abstract data type); Color constancy; Code (set theory); Balance (ability); Color correction; Computer graphics (images); Image (mathematics); Image processing; Color image","retraction":null,"screen_n_in":null,"score":{"opus":0.0183484252751136,"gpt":0.3053997954409911,"spread":0.2870513701658775,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003720238,0.0004952063,0.000648532,0.000348663,0.0005175788,0.0002332778,0.001660065,0.0001435057,0.001367436],"category_scores_gemma":[0.000004237127,0.0005091499,0.0004685769,0.0005297614,0.0002037379,0.0001369171,0.0009118887,0.0006923782,0.00009336794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001219023,"about_ca_system_score_gemma":0.0003484352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000790419,"about_ca_topic_score_gemma":0.00001370522,"domain_scores_codex":[0.996797,0.00009291933,0.0008557241,0.001313094,0.0005113111,0.0004299856],"domain_scores_gemma":[0.9967278,0.0002027631,0.0008122416,0.001625081,0.0004693413,0.0001627883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002191757,0.003362925,0.004068244,0.0003099267,0.0003086144,0.000001157931,0.001427763,0.02126068,0.005890975,0.09663201,0.2251947,0.6413238],"study_design_scores_gemma":[0.001408849,0.001143229,0.00482644,0.0006362905,0.0002210761,0.000004790936,0.001266771,0.6590648,0.008325608,0.03355142,0.2877022,0.001848562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02088018,0.00003306755,0.9659593,0.002102661,0.002509753,0.003125538,0.0009236655,0.0001564807,0.004309363],"genre_scores_gemma":[0.9790674,0.00002516807,0.007709724,0.0001699849,0.0007785715,0.007705414,0.0009251441,0.00005440377,0.003564145],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9582496,"threshold_uncertainty_score":0.999736,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4213150508","doi":"10.1109/wacv51458.2022.00203","title":"VCSeg: Virtual Camera Adaptation for Road Segmentation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Segmentation; Mean-shift; Generalization; Camera auto-calibration; Camera resectioning; Image segmentation; Domain (mathematical analysis); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01936086054548443,"gpt":0.2752015544924593,"spread":0.2558406939469749,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003416517,0.0002323286,0.00025125,0.0001615269,0.0005628988,0.00007184585,0.0005966887,0.0000586002,0.001397616],"category_scores_gemma":[0.00000604397,0.000248624,0.0001550348,0.0004981267,0.0001609914,0.0001660903,0.0002489093,0.0002415585,0.0002545484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002236329,"about_ca_system_score_gemma":0.00004582527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001363416,"about_ca_topic_score_gemma":0.00002746992,"domain_scores_codex":[0.9977997,0.0001305037,0.0005428812,0.0006753845,0.0005881476,0.0002633838],"domain_scores_gemma":[0.9986313,0.0001260273,0.0003486268,0.0007049449,0.00007540289,0.000113699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001538834,0.0007222189,0.00007934059,0.00001471971,0.00004138498,7.449454e-7,0.001952933,0.04811658,0.05441997,0.005785119,0.03330086,0.8554122],"study_design_scores_gemma":[0.001855095,0.002282996,0.003107465,0.00004492669,0.00008078461,0.00002598615,0.002431661,0.8515781,0.01160899,0.004511729,0.1216526,0.0008196741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07846099,0.000009722574,0.9140489,0.001705224,0.000310495,0.001772105,0.000146147,0.0001156909,0.003430704],"genre_scores_gemma":[0.9729104,0.00001152428,0.02390956,0.0006019579,0.0001180672,0.0006756018,0.0002987038,0.00003415773,0.001440036],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8944494,"threshold_uncertainty_score":0.9999966,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4213325824","doi":"10.1109/wacv51458.2022.00176","title":"Towards Durability Estimation of Bioprosthetic Heart Valves Via Motion Symmetry Analysis","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"","keywords":"Asymmetry; Symmetry (geometry); Dynamic time warping; Pulsatile flow; Rotational symmetry; Computer science; Diagonal; Artificial intelligence; Computer vision; Mathematics; Algorithm; Physics; Geometry; Cardiology; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.01402469646294983,"gpt":0.3367853586677537,"spread":0.3227606622048038,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002758176,0.0002206777,0.0006245581,0.000557251,0.0001480679,0.00002487262,0.0002238504,0.00005617409,0.0008468382],"category_scores_gemma":[0.00001008221,0.0002087195,0.001191603,0.001080671,0.0001192577,0.00007813623,0.0001649193,0.0001841071,0.00003334834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001494075,"about_ca_system_score_gemma":0.0001054664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000927494,"about_ca_topic_score_gemma":0.00000120144,"domain_scores_codex":[0.9978393,0.0001431197,0.0006299363,0.0005482518,0.0006601346,0.0001792627],"domain_scores_gemma":[0.9981815,0.00006547201,0.0003081725,0.000990196,0.0003138677,0.0001407667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001918012,0.01673181,0.1276436,0.0009472273,0.007941667,0.00001019353,0.001207495,0.02343869,0.02032535,0.01254684,0.003314017,0.7839751],"study_design_scores_gemma":[0.002451864,0.002913128,0.7361349,0.0001299817,0.003785661,0.00002665839,0.000249419,0.2408285,0.007562193,0.005076073,0.0003852985,0.0004563337],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6813358,0.00008193721,0.3150927,0.001217252,0.000166538,0.001263552,0.00031496,0.00007345753,0.0004538267],"genre_scores_gemma":[0.9952155,0.00001390334,0.003636087,0.000158114,0.00003324226,0.0003486124,0.0005005701,0.00001841627,0.00007553845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7835188,"threshold_uncertainty_score":0.9272284,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}