{"meta":{"query_hash":"93080e469fcb","filters":{"venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)"},"cohort_total":22,"direct_labels_cover":0,"predictions_cover":22,"exported":22,"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/93080e469fcb","api":"https://metacan.xera.ac/api/v1/cohort?venue=2023+IEEE%2FCVF+Winter+Conference+on+Applications+of+Computer+Vision+%28WACV%29"},"results":[{"id":"W3195180572","doi":"10.1109/wacv56688.2023.00529","title":"Serf: Towards better training of deep neural networks using log-Softplus ERror activation Function","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Neural Network Applications","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 Toronto","funders":"","keywords":"Computer science; Activation function; Artificial intelligence; Regularization (linguistics); Artificial neural network; Monotonic function; Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.06425871713562263,"score_gpt":0.32289148878931506,"score_spread":0.2586327716536924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195180572","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028032325,0.00065632624,0.9657669,0.0003362822,0.000073326824,0.00006537024,0.000106905114,0.0029724624,0.0019901046],"genre_scores_gemma":[0.4599517,0.00047195377,0.530527,0.0006403134,0.00007524719,0.00028773487,0.0007342489,0.00065287296,0.006658954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994703,0.00018181518,0.000034543664,0.00009311502,0.00014013791,0.00008011049],"domain_scores_gemma":[0.99876595,0.0005933136,0.00008381466,0.00016772581,0.00032234835,0.000066867105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029087688,0.0015833827,0.00092100486,0.0007726081,0.00034114282,0.00072380726,0.0019363677,0.0020295563,0.003633079],"category_scores_gemma":[0.006015085,0.0005922977,0.00077059964,0.0004947541,0.0007742565,0.0020493174,0.0014686392,0.0026127247,0.0013865491],"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":[0.00024289523,0.00018715918,0.0013400274,0.00020805691,0.00009333177,0.00015394427,0.00012626548,0.69876957,0.009216529,0.0093484465,0.0059723533,0.27434137],"study_design_scores_gemma":[0.000007836539,0.00003836262,0.00008327267,0.000012554261,0.000003552467,0.000022796547,0.000006877534,0.99615896,0.0016218448,0.0015319191,0.00050797727,0.000004086853],"about_ca_topic_score_codex":0.0040862025,"about_ca_topic_score_gemma":0.006621227,"teacher_disagreement_score":0.0040862025,"about_ca_system_score_codex":0.0008048318,"about_ca_system_score_gemma":0.0014822793,"threshold_uncertainty_score":0.015383184},"labels":[],"label_agreement":null},{"id":"W4319299723","doi":"10.1109/wacv56688.2023.00073","title":"Dense Voxel Fusion for 3D Object Detection","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":74,"is_retracted":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":"Voxel; Artificial intelligence; Computer science; Point cloud; Computer vision; Lidar; Object detection; Benchmark (surveying); Pixel; Point (geometry); Feature (linguistics); Sensor fusion; Detector; Ground truth; Pattern recognition (psychology); Image fusion; Fusion; Image (mathematics); Mathematics; Remote sensing","score_opus":0.03079439693062873,"score_gpt":0.3126943239145511,"score_spread":0.28189992698392236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319299723","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0162622,0.0005219329,0.96892035,0.00013497924,0.000057398407,0.00008399135,0.0011559251,0.011301475,0.0015617831],"genre_scores_gemma":[0.29080763,0.00041472752,0.69639707,0.00021887966,0.00007257837,0.00022879337,0.008607724,0.0007954506,0.0024571954],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987111,0.00018140161,0.000043536595,0.0003945914,0.00051982625,0.00014952061],"domain_scores_gemma":[0.99905866,0.00024666495,0.00007723498,0.000300448,0.0002713131,0.000045665176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010236284,0.0014672483,0.0014245334,0.0020129632,0.00052948983,0.0014215715,0.0024441588,0.0014575779,0.004287846],"category_scores_gemma":[0.0033621294,0.000819963,0.0014373398,0.0020787655,0.0007439297,0.0020754426,0.003056564,0.0015837732,0.0033460858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004044286,0.00024947283,0.002792393,0.00022128115,0.00015496119,0.00014035242,0.0001434178,0.15454502,0.027873723,0.006220415,0.020274336,0.78698015],"study_design_scores_gemma":[0.000018664148,0.000083434985,0.0009883862,0.0000193666,0.000023895771,0.000222637,0.000055584027,0.9584225,0.02388772,0.009187597,0.0070645506,0.00002577483],"about_ca_topic_score_codex":0.0067259828,"about_ca_topic_score_gemma":0.011032451,"teacher_disagreement_score":0.0067259828,"about_ca_system_score_codex":0.0010611285,"about_ca_system_score_gemma":0.0012635593,"threshold_uncertainty_score":0.014344275},"labels":[],"label_agreement":null},{"id":"W4319299834","doi":"10.1109/wacv56688.2023.00170","title":"AttTrack: Online Deep Attention Transfer for Multi-object Tracking","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Video Surveillance and Tracking Methods","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; Inference; Video tracking; Component (thermodynamics); Artificial intelligence; Object detection; Tracking (education); Object (grammar); Key (lock); Deep learning; Transfer of learning; Machine learning; Analytics; Interleaving; Visual analytics; Real-time computing; Visualization; Data mining; Pattern recognition (psychology); Computer security","score_opus":0.09286112842069297,"score_gpt":0.3786234061387497,"score_spread":0.28576227771805673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319299834","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038448807,0.001229462,0.9274907,0.00039258602,0.00033665966,0.00015038527,0.00062744913,0.02720299,0.0041208873],"genre_scores_gemma":[0.65890354,0.0004673129,0.3167603,0.00088844885,0.00023276139,0.00033638944,0.0032743565,0.0010505881,0.018086439],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952877,0.00005548347,0.000013832097,0.00021546354,0.00009977619,0.000086764216],"domain_scores_gemma":[0.9993358,0.00023378458,0.000050624883,0.00015880643,0.00014027175,0.00008066637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011722288,0.0016885896,0.0009618432,0.0007910139,0.0006800208,0.000909351,0.0033686322,0.0018129194,0.006255667],"category_scores_gemma":[0.002786625,0.0006336729,0.0007995382,0.00091429,0.00051251263,0.002730132,0.0026360168,0.0026897846,0.0025454247],"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":[0.00043904647,0.0005695897,0.0021771002,0.00014089089,0.00019078866,0.00019162054,0.0001498709,0.15872322,0.016138911,0.004991994,0.029453818,0.7868331],"study_design_scores_gemma":[0.000025653368,0.0000781173,0.00028489283,0.000007669026,0.000015658992,0.00003512725,0.0000126935975,0.98999995,0.0038873798,0.0040931148,0.0015508474,0.00000886927],"about_ca_topic_score_codex":0.014556023,"about_ca_topic_score_gemma":0.019256592,"teacher_disagreement_score":0.014556023,"about_ca_system_score_codex":0.0012712658,"about_ca_system_score_gemma":0.0015274523,"threshold_uncertainty_score":0.028942585},"labels":[],"label_agreement":null},{"id":"W4319299845","doi":"10.1109/wacv56688.2023.00523","title":"Hyperspherical Quantization: Toward Smaller and More Accurate Models","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Quantization (signal processing); Vector quantization; Computer science; Algorithm; Linde–Buzo–Gray algorithm; Computation; Estimator; Ternary operation; Learning vector quantization; Mathematics","score_opus":0.06798127665387065,"score_gpt":0.32911624943362666,"score_spread":0.261134972779756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319299845","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04948902,0.00083490706,0.9405193,0.0009020708,0.00015821504,0.0000763513,0.0008340607,0.0046632173,0.0025229158],"genre_scores_gemma":[0.5154283,0.00053987314,0.47665447,0.00072499586,0.00008697448,0.00014854224,0.0023548969,0.00072677043,0.0033351225],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991509,0.00015009876,0.00005524753,0.00017451891,0.00041422588,0.000054950116],"domain_scores_gemma":[0.99836534,0.00051779463,0.0001410268,0.00060201256,0.00031580328,0.000058029495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083029066,0.0008611645,0.00089343754,0.00077879813,0.00036275949,0.0013903691,0.0017446489,0.0010108,0.0036747938],"category_scores_gemma":[0.006769738,0.00031642063,0.00049019646,0.00087019167,0.0008448947,0.0036334463,0.0020364076,0.0018692572,0.0008731825],"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":[0.00038836207,0.00015405584,0.0015192945,0.00023655743,0.000071581424,0.00019353065,0.00019255052,0.35259128,0.033116125,0.03879208,0.016178308,0.5565663],"study_design_scores_gemma":[0.000014325527,0.000034222365,0.00016145894,0.00001115354,0.0000060189727,0.000048268954,0.000017467608,0.976982,0.008284084,0.012552354,0.0018761422,0.000012541399],"about_ca_topic_score_codex":0.005326925,"about_ca_topic_score_gemma":0.006384024,"teacher_disagreement_score":0.005326925,"about_ca_system_score_codex":0.00092585623,"about_ca_system_score_gemma":0.0012493839,"threshold_uncertainty_score":0.012293458},"labels":[],"label_agreement":null},{"id":"W4319299896","doi":"10.1109/wacv56688.2023.00519","title":"Towards Disturbance-Free Visual Mobile Manipulation","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Robot Manipulation and Learning","field":"Engineering","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":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Reinforcement learning; Computer science; Task (project management); Collision avoidance; Disturbance (geology); Artificial intelligence; Local optimum; Constraint (computer-aided design); Collision; Human–computer interaction; Machine learning; Computer security; Engineering","score_opus":0.02713769580051091,"score_gpt":0.2980436752208911,"score_spread":0.2709059794203802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319299896","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08184531,0.00046994784,0.9031678,0.0004034967,0.000087612,0.00006596375,0.00006822865,0.0035467823,0.010344736],"genre_scores_gemma":[0.8095326,0.00023889133,0.18164691,0.00026059384,0.00003997216,0.000102517246,0.0001759213,0.00025558102,0.0077469884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998559,0.0000333204,0.000005340999,0.000031368498,0.00004018786,0.00003392918],"domain_scores_gemma":[0.9996817,0.00013992995,0.000045905093,0.000049670332,0.000044200508,0.000038654234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033472505,0.0006866045,0.0003525438,0.00020732134,0.00023070197,0.00044025716,0.0009895811,0.0007407851,0.003956634],"category_scores_gemma":[0.0011264102,0.00026824285,0.000340629,0.00015126188,0.00059878494,0.0006818963,0.0010792259,0.0011392838,0.00084466377],"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":[0.00021397177,0.00016131533,0.0010019502,0.00013020728,0.000031930336,0.00013078848,0.00015324517,0.7975065,0.026231691,0.0092695,0.0033513312,0.16181754],"study_design_scores_gemma":[0.00001196039,0.000065051485,0.000121434394,0.0000062415875,0.0000028397094,0.000019468029,0.000010037695,0.9944706,0.0023924387,0.0018151104,0.0010815137,0.0000031975796],"about_ca_topic_score_codex":0.003176872,"about_ca_topic_score_gemma":0.003605283,"teacher_disagreement_score":0.003956634,"about_ca_system_score_codex":0.00047082148,"about_ca_system_score_gemma":0.00057862396,"threshold_uncertainty_score":0.013236284},"labels":[],"label_agreement":null},{"id":"W4319299923","doi":"10.1109/wacv56688.2023.00224","title":"AudioViewer: Learning to Visualize Sounds","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Music and Audio Processing","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; Speech recognition; Perception; Field (mathematics); Visualization; Substitution (logic); Natural language processing; Parsing; Artificial intelligence; Human–computer interaction; Multimedia","score_opus":0.03231023235635655,"score_gpt":0.3323631389603665,"score_spread":0.30005290660400996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319299923","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024761176,0.00093094085,0.89888763,0.00042254644,0.0003475464,0.00024435736,0.0038678704,0.061332077,0.009205897],"genre_scores_gemma":[0.18476455,0.0009044745,0.78343314,0.0004853253,0.00014342577,0.00069995737,0.010272294,0.0031209686,0.016175807],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99961615,0.00008327845,0.000011250903,0.00017872399,0.00008067438,0.000029957122],"domain_scores_gemma":[0.9993728,0.0003122987,0.000031588705,0.00012503017,0.00009119389,0.0000672069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058293866,0.0015899674,0.0004803574,0.0006939483,0.00025400645,0.0010132939,0.0020498207,0.0010722143,0.017234756],"category_scores_gemma":[0.0035092384,0.00045802572,0.0008648801,0.00035751506,0.0005063354,0.0023871004,0.002133305,0.0014083284,0.006154065],"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":[0.00047698838,0.00022103338,0.0013188634,0.00042674242,0.00008446801,0.00020185327,0.0002757539,0.028693052,0.03552578,0.007859519,0.06264543,0.8622705],"study_design_scores_gemma":[0.00015173925,0.00043934854,0.0015101916,0.00015424378,0.000051201867,0.00046115115,0.0002671114,0.8448673,0.050332464,0.034516286,0.0671654,0.000083567655],"about_ca_topic_score_codex":0.0016826374,"about_ca_topic_score_gemma":0.003401746,"teacher_disagreement_score":0.017234756,"about_ca_system_score_codex":0.00039505758,"about_ca_system_score_gemma":0.00040663974,"threshold_uncertainty_score":0.05765593},"labels":[],"label_agreement":null},{"id":"W4319299947","doi":"10.1109/wacv56688.2023.00204","title":"A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"AI in cancer detection","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Histopathology; Probabilistic logic; Pattern recognition (psychology); Machine learning; Pathology; Medicine","score_opus":0.03419935978624389,"score_gpt":0.2980842172225716,"score_spread":0.2638848574363277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319299947","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012742205,0.00019819559,0.98421335,0.00029902474,0.00004748626,0.000028838951,0.00012580788,0.00031893948,0.0020261714],"genre_scores_gemma":[0.7297181,0.0007799351,0.25644594,0.00028026538,0.00008082828,0.00014809554,0.0004841379,0.00022451249,0.011838191],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998441,0.000029668798,0.0000063375364,0.000049919418,0.000054677257,0.00001528367],"domain_scores_gemma":[0.99965453,0.00017692018,0.000056117253,0.0000314458,0.000057208465,0.000023831664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046835397,0.0005740906,0.0003575984,0.0004391655,0.00018154616,0.0006042456,0.00074168434,0.00096992386,0.002244874],"category_scores_gemma":[0.001492604,0.0004061007,0.0007464055,0.00031870732,0.00050411513,0.0006772207,0.0006666512,0.00089605135,0.00052585296],"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":[0.000059353613,0.000019238063,0.00031776444,0.000041191106,0.000015142223,0.00009287278,0.000038588332,0.9454303,0.017704168,0.015825482,0.0007143753,0.019741472],"study_design_scores_gemma":[0.0000028580116,0.000009632102,0.000057997833,0.0000023665336,0.0000029231044,0.000031008527,0.0000014514213,0.99600285,0.0012604493,0.0022524565,0.00037157148,0.0000044558074],"about_ca_topic_score_codex":0.0029160287,"about_ca_topic_score_gemma":0.0022949567,"teacher_disagreement_score":0.0029160287,"about_ca_system_score_codex":0.00075074524,"about_ca_system_score_gemma":0.0004852601,"threshold_uncertainty_score":0.0075098276},"labels":[],"label_agreement":null},{"id":"W4319299982","doi":"10.1109/wacv56688.2023.00124","title":"Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Clouds","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Perception; Feature (linguistics); Artificial intelligence; Pedestrian detection; Pedestrian; Lidar; Fusion; Feature selection; Computer vision; Sensor fusion; Point cloud; Adaptation (eye); Object detection; Pattern recognition (psychology); Engineering; Remote sensing; Geography; Transport engineering","score_opus":0.04618363410657625,"score_gpt":0.33074855624856675,"score_spread":0.2845649221419905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319299982","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09948326,0.0006346739,0.8910544,0.00017934796,0.00010881791,0.00013462167,0.00081500737,0.005988194,0.0016016094],"genre_scores_gemma":[0.7680744,0.00024032407,0.2275944,0.00015734886,0.00005520352,0.00011224355,0.0024055704,0.00013760869,0.001222903],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990428,0.00009505279,0.000034404704,0.00037236008,0.0003047424,0.00015057079],"domain_scores_gemma":[0.9994259,0.00009737373,0.00005472574,0.00015623981,0.00022735208,0.00003848539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008743581,0.0014777009,0.0011568563,0.0019218149,0.00052413903,0.00094146666,0.0020234885,0.00082238246,0.00095447607],"category_scores_gemma":[0.0015644514,0.00048810343,0.0015056295,0.0017676987,0.00039751313,0.0018511869,0.002018231,0.0010346649,0.00084299245],"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":[0.00041689305,0.00042936392,0.0095811365,0.00014390027,0.00033076658,0.00025826914,0.00028443197,0.17236538,0.055372793,0.0019247603,0.00920678,0.7496855],"study_design_scores_gemma":[0.000019399457,0.00011710533,0.0043306113,0.000013093398,0.000047551184,0.00013037918,0.00010470548,0.9746149,0.015319841,0.0026636,0.002607012,0.00003170061],"about_ca_topic_score_codex":0.011792771,"about_ca_topic_score_gemma":0.010215296,"teacher_disagreement_score":0.011792771,"about_ca_system_score_codex":0.0007156251,"about_ca_system_score_gemma":0.0007391023,"threshold_uncertainty_score":0.023448288},"labels":[],"label_agreement":null},{"id":"W4319299985","doi":"10.1109/wacv56688.2023.00542","title":"Event-based RGB sensing with structured light","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Polytechnique Montréal","funders":"","keywords":"Monochrome; Computer science; Computer vision; Projector; Artificial intelligence; RGB color model; Pixel; Brightness; Digital Light Processing; Computer graphics (images); Optics","score_opus":0.016188930273947865,"score_gpt":0.27046898473788544,"score_spread":0.2542800544639376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319299985","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043475673,0.0003165042,0.9460645,0.00014648907,0.00010761326,0.000099862285,0.00041350676,0.0026290608,0.0067467885],"genre_scores_gemma":[0.48171678,0.00050012424,0.5117333,0.00032360922,0.00008258163,0.0001751306,0.0006371876,0.00029343084,0.004537912],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99968004,0.000030169771,0.000012086331,0.00009552448,0.00015658747,0.000025608098],"domain_scores_gemma":[0.99981076,0.000054697495,0.00002534333,0.000046673125,0.000045555695,0.000016995116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014577109,0.0004638824,0.0002879968,0.00028402414,0.00013286686,0.0006713735,0.00081200094,0.00033712326,0.0027571085],"category_scores_gemma":[0.00056330365,0.00029842954,0.00033525936,0.00032908365,0.0003301735,0.00082363683,0.0011249792,0.00050807535,0.0007068504],"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":[0.0004716563,0.00016116082,0.001397265,0.00040076106,0.00006274487,0.00028176646,0.0002709427,0.043378316,0.6911495,0.014035299,0.006384733,0.24200585],"study_design_scores_gemma":[0.00006620493,0.00016651442,0.002077565,0.0000406649,0.000027236836,0.00034995962,0.000058844977,0.67701626,0.29862568,0.008965359,0.012525743,0.00008001368],"about_ca_topic_score_codex":0.0009364059,"about_ca_topic_score_gemma":0.0012836887,"teacher_disagreement_score":0.0027571085,"about_ca_system_score_codex":0.0003386606,"about_ca_system_score_gemma":0.0002608019,"threshold_uncertainty_score":0.009223402},"labels":[],"label_agreement":null},{"id":"W4319300112","doi":"10.1109/wacv56688.2023.00420","title":"Learning Style Subspaces for Controllable Unpaired Domain Translation","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Generative Adversarial Networks and Image Synthesis","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; Linear subspace; Translation (biology); Artificial intelligence; Robustness (evolution); Domain (mathematical analysis); Image translation; Theoretical computer science; Algorithm; Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.028954898761440452,"score_gpt":0.28492549276178025,"score_spread":0.2559705940003398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319300112","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018706031,0.00083615875,0.970392,0.00013176363,0.00013766777,0.000117186515,0.00071611354,0.0056110765,0.0033519585],"genre_scores_gemma":[0.38971797,0.0009282427,0.5863258,0.00050435663,0.00023492605,0.0004502277,0.009698684,0.0010905007,0.011049242],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990571,0.00025017938,0.000046177753,0.00034655584,0.00022243035,0.000077521996],"domain_scores_gemma":[0.99907935,0.00025060284,0.00006044645,0.0004091223,0.00013865813,0.00006181478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010222957,0.001414874,0.000937357,0.0008225365,0.0004310815,0.001001677,0.0010954154,0.0009923021,0.006196328],"category_scores_gemma":[0.0029751372,0.00032015357,0.001545698,0.0011567385,0.00072133454,0.0013733676,0.0015894672,0.0017629971,0.004422399],"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":[0.0003850987,0.00023321071,0.001285359,0.00024074144,0.00011679439,0.00018907984,0.00016381124,0.09936955,0.028864082,0.010254747,0.018332452,0.84056497],"study_design_scores_gemma":[0.00008970223,0.00022620238,0.0009439482,0.00003794937,0.00003492241,0.0003454904,0.000111813955,0.9432146,0.019350145,0.021839498,0.01376343,0.000042355125],"about_ca_topic_score_codex":0.0017207093,"about_ca_topic_score_gemma":0.0024469844,"teacher_disagreement_score":0.006196328,"about_ca_system_score_codex":0.00045150274,"about_ca_system_score_gemma":0.0006904898,"threshold_uncertainty_score":0.020728767},"labels":[],"label_agreement":null},{"id":"W4319300119","doi":"10.1109/wacv56688.2023.00301","title":"MixVPR: Feature Mixing for Visual Place Recognition","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":226,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Nature","keywords":"Computer science; Feature (linguistics); Artificial intelligence; Margin (machine learning); Process (computing); Cascade; Pattern recognition (psychology); Latency (audio); Set (abstract data type); Scale (ratio); Feature extraction; Precision and recall; Computer vision; Machine learning; Engineering","score_opus":0.03833107397971237,"score_gpt":0.35126698874967427,"score_spread":0.3129359147699619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319300119","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034593653,0.0019050004,0.88781697,0.0001755634,0.0002853654,0.00032817398,0.002663745,0.067604646,0.004626858],"genre_scores_gemma":[0.29189655,0.00078595354,0.6764772,0.00038629066,0.00019051864,0.00031576437,0.012921422,0.0019382908,0.015088071],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989975,0.00006861485,0.0000462707,0.0003580605,0.00039293015,0.00013656146],"domain_scores_gemma":[0.9995442,0.00006141637,0.000045201887,0.00020112567,0.000112201626,0.000035774963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006450074,0.0020385843,0.0017730918,0.003306094,0.00057103153,0.0013337415,0.0036470715,0.0012206179,0.008699508],"category_scores_gemma":[0.0013641268,0.0006973908,0.0013158101,0.0027504314,0.000544949,0.002877244,0.002917718,0.0012235997,0.007738328],"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":[0.00027841615,0.00016075792,0.0008587107,0.00017831466,0.000102330145,0.00014103846,0.00006765486,0.00874571,0.033888802,0.0017416957,0.02008795,0.9337486],"study_design_scores_gemma":[0.00022565156,0.00081072893,0.0046711816,0.00007393586,0.00018919405,0.0018884927,0.0002955114,0.76566225,0.15773886,0.014716384,0.053588964,0.00013881059],"about_ca_topic_score_codex":0.005663404,"about_ca_topic_score_gemma":0.009606825,"teacher_disagreement_score":0.008699508,"about_ca_system_score_codex":0.0007237904,"about_ca_system_score_gemma":0.00075009844,"threshold_uncertainty_score":0.029102743},"labels":[],"label_agreement":null},{"id":"W4319300245","doi":"10.1109/wacv56688.2023.00286","title":"TransVLAD: Multi-Scale Attention-Based Global Descriptors for Visual Geo-Localization","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Discriminative model; ENCODE; Artificial intelligence; Embedding; Pattern recognition (psychology); Convolutional neural network; Feature (linguistics); Feature learning; Code (set theory); Scale (ratio)","score_opus":0.03612441536544376,"score_gpt":0.35683198803777455,"score_spread":0.3207075726723308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319300245","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020236399,0.0011231443,0.96621734,0.00015268027,0.00019847072,0.00014864601,0.001407932,0.008494542,0.0020207453],"genre_scores_gemma":[0.5195516,0.0012788732,0.45370492,0.0005288661,0.00024662036,0.00033599947,0.009887612,0.0008601192,0.013605456],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996886,0.000029136516,0.000012359245,0.00012206179,0.000085760395,0.0000619526],"domain_scores_gemma":[0.99972504,0.000044275625,0.000034566012,0.00008606323,0.00008276833,0.00002718056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004298519,0.0013038098,0.0014715063,0.0018761721,0.00030461882,0.00097229745,0.0022097016,0.0006799997,0.0035390228],"category_scores_gemma":[0.00093631534,0.0003940753,0.0009928657,0.0016855957,0.00045210356,0.0017643077,0.0021017452,0.0010776052,0.0020031673],"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":[0.00030012257,0.0002596672,0.0024087012,0.00026771988,0.00017397937,0.00012397583,0.00009516323,0.059704773,0.029859664,0.006576491,0.026587758,0.87364197],"study_design_scores_gemma":[0.000050355695,0.00018884656,0.0026215487,0.000037931783,0.00008729674,0.0002691212,0.00008433153,0.9532351,0.020105872,0.008477908,0.014794281,0.000047372203],"about_ca_topic_score_codex":0.009837716,"about_ca_topic_score_gemma":0.013672292,"teacher_disagreement_score":0.009837716,"about_ca_system_score_codex":0.0008379032,"about_ca_system_score_gemma":0.0009117898,"threshold_uncertainty_score":0.019560933},"labels":[],"label_agreement":null},{"id":"W4319300559","doi":"10.1109/wacv56688.2023.00041","title":"RAST: Restorable Arbitrary Style Transfer via Multi-restoration","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Stylized fact; Computer science; Style (visual arts); Embedding; Architecture; Perspective (graphical); Image (mathematics); Artificial intelligence; Art","score_opus":0.03313096733209031,"score_gpt":0.28596619007016316,"score_spread":0.25283522273807285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319300559","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020194905,0.00028713432,0.9733098,0.00010639066,0.00010223083,0.000073207615,0.000049491544,0.0019516587,0.003925089],"genre_scores_gemma":[0.59482354,0.000479862,0.39089033,0.00038121833,0.00011545642,0.00014567125,0.00026171806,0.00050324324,0.012399018],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995766,0.00006657992,0.00001951368,0.000101538906,0.00018913155,0.000046616336],"domain_scores_gemma":[0.9995035,0.00009247774,0.00006410482,0.00020924161,0.00008839659,0.000042293228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006702093,0.00092993985,0.000587834,0.0006428426,0.00037941476,0.0007238235,0.0013838869,0.0008535782,0.003287661],"category_scores_gemma":[0.0012646188,0.00029557588,0.00091785553,0.00036826511,0.0009657894,0.0011323548,0.0014163649,0.0012876169,0.001403306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037628764,0.00020921422,0.0010347491,0.00021981614,0.00014771598,0.00042890836,0.0002757304,0.30827165,0.17709479,0.01928972,0.005605881,0.4870456],"study_design_scores_gemma":[0.00002038781,0.00016151428,0.0003403386,0.00001737494,0.00003146822,0.00037755314,0.000019479749,0.9467837,0.040670563,0.007567539,0.003979825,0.000030218107],"about_ca_topic_score_codex":0.00086386956,"about_ca_topic_score_gemma":0.001231611,"teacher_disagreement_score":0.003287661,"about_ca_system_score_codex":0.0004810037,"about_ca_system_score_gemma":0.0004597295,"threshold_uncertainty_score":0.010998368},"labels":[],"label_agreement":null},{"id":"W4319300616","doi":"10.1109/wacv56688.2023.00355","title":"FastSwap: A Lightweight One-Stage Framework for Real-Time Face Swapping","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Face recognition and analysis","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Normalization (sociology); Block (permutation group theory); High fidelity; Face (sociological concept); Fidelity; Computation; Artificial intelligence; Facial recognition system; Machine learning; Pattern recognition (psychology); Algorithm","score_opus":0.040442617715713544,"score_gpt":0.319879702277053,"score_spread":0.2794370845613394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319300616","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008686221,0.00024541558,0.9690316,0.00006441716,0.000100522215,0.00018952983,0.00033811425,0.019384908,0.001959252],"genre_scores_gemma":[0.17080526,0.00040563603,0.81516314,0.0003703304,0.000065176544,0.00051040767,0.002474133,0.0025207782,0.007685143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956614,0.000047693513,0.000013991745,0.000119845354,0.000184016,0.00006833682],"domain_scores_gemma":[0.9997403,0.0000600297,0.00001529188,0.00010252575,0.000054216544,0.0000276746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006785875,0.0015337024,0.0009941775,0.0007530546,0.00042959693,0.0007516268,0.003319524,0.0010178249,0.012734699],"category_scores_gemma":[0.0013510797,0.00064924447,0.0009805369,0.00041879408,0.0005219785,0.001391959,0.0023053228,0.0016311015,0.0039803586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057478074,0.00022528261,0.00074896583,0.00021585237,0.00012587376,0.00029088027,0.00011397913,0.07070954,0.0689099,0.0046140514,0.026029134,0.82744175],"study_design_scores_gemma":[0.000053502812,0.00011841174,0.00044746287,0.000016444035,0.000021622389,0.00036054107,0.000030193043,0.94897676,0.03494755,0.005164887,0.009823823,0.000038833914],"about_ca_topic_score_codex":0.0044820993,"about_ca_topic_score_gemma":0.0076600257,"teacher_disagreement_score":0.012734699,"about_ca_system_score_codex":0.00048621924,"about_ca_system_score_gemma":0.0009157651,"threshold_uncertainty_score":0.042601824},"labels":[],"label_agreement":null},{"id":"W4319300623","doi":"10.1109/wacv56688.2023.00426","title":"Multivariate Probabilistic Monocular 3D Object Detection","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Neural Network Applications","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Monocular; Artificial intelligence; Robustness (evolution); Probabilistic logic; Computer science; Covariance; Probability distribution; Multivariate statistics; Covariance matrix; Joint probability distribution; Computer vision; Posterior probability; Object detection; Pattern recognition (psychology); Mathematics; Machine learning; Algorithm; Bayesian probability; Statistics","score_opus":0.02941803805642603,"score_gpt":0.30584552395998427,"score_spread":0.27642748590355826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319300623","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01777056,0.00038206583,0.97826034,0.00016183264,0.000044069257,0.0000338386,0.00040452546,0.0019331861,0.0010095435],"genre_scores_gemma":[0.5977948,0.00066080946,0.39435494,0.00034765436,0.00010626872,0.000110884765,0.0024081706,0.00031543098,0.003901021],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989919,0.00012677151,0.000030501074,0.000357613,0.00038407953,0.000109138375],"domain_scores_gemma":[0.99907327,0.00026230255,0.00011760063,0.00024206097,0.00025084426,0.00005400147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008369396,0.0011469699,0.0011955041,0.00083723915,0.0003486596,0.0007495225,0.0020075573,0.00090141926,0.0018266783],"category_scores_gemma":[0.0026129843,0.0007232877,0.000920946,0.0012076056,0.0005022953,0.0013991266,0.0016790744,0.001025125,0.00092779053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026307898,0.00013952718,0.0057338295,0.0001467201,0.00015875333,0.00014512308,0.00008807844,0.3084724,0.02226574,0.0058585536,0.008504009,0.6482241],"study_design_scores_gemma":[0.0000040900295,0.000016490161,0.0015821945,0.0000043483865,0.0000072172215,0.00008247968,0.0000065210183,0.9924274,0.0024697648,0.0024572029,0.0009313895,0.00001088564],"about_ca_topic_score_codex":0.007714758,"about_ca_topic_score_gemma":0.012959433,"teacher_disagreement_score":0.007714758,"about_ca_system_score_codex":0.000659148,"about_ca_system_score_gemma":0.0008649729,"threshold_uncertainty_score":0.015339673},"labels":[],"label_agreement":null},{"id":"W4319300710","doi":"10.1109/wacv56688.2023.00207","title":"BoxMask: Revisiting Bounding Box Supervision for Video Object Detection","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"CODE","keywords":"Computer science; Discriminative model; Minimum bounding box; Artificial intelligence; Bounding overwatch; Representation (politics); Object detection; Computer vision; Confusion; Pixel; Class (philosophy); Detector; Object (grammar); Feature (linguistics); Feature extraction; EPIC; Pattern recognition (psychology); Image (mathematics)","score_opus":0.03525540149132339,"score_gpt":0.32025917711750057,"score_spread":0.2850037756261772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319300710","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01079038,0.00049647887,0.96862954,0.0001432582,0.00013644825,0.00012484544,0.0004063446,0.017878179,0.0013944365],"genre_scores_gemma":[0.17653672,0.0005155829,0.81193274,0.00050312077,0.00025274855,0.0002844212,0.002746937,0.002152536,0.0050751767],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998492,0.00021637653,0.000045955658,0.00057916937,0.00050770986,0.00015887374],"domain_scores_gemma":[0.99846184,0.00046679127,0.00012958408,0.00049838505,0.00030637073,0.00013695202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014444195,0.002040305,0.0021364123,0.0016366022,0.00065881707,0.0012525002,0.004169106,0.0014617454,0.007386396],"category_scores_gemma":[0.005725937,0.0010377131,0.0010911729,0.0011439109,0.0010449463,0.0036842173,0.0033460814,0.0030368187,0.0042498484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005136168,0.00029476197,0.0019678252,0.0002471131,0.000120679004,0.00015226679,0.00017082533,0.03207712,0.04309199,0.006569174,0.024262037,0.89053255],"study_design_scores_gemma":[0.000035257726,0.00012931385,0.0008434316,0.00004140938,0.000033688844,0.00022904306,0.000038571514,0.94502,0.032611884,0.010794575,0.010183547,0.000039218183],"about_ca_topic_score_codex":0.0061696703,"about_ca_topic_score_gemma":0.008712373,"teacher_disagreement_score":0.007386396,"about_ca_system_score_codex":0.0007532997,"about_ca_system_score_gemma":0.0015489572,"threshold_uncertainty_score":0.02470994},"labels":[],"label_agreement":null},{"id":"W4319300757","doi":"10.1109/wacv56688.2023.00347","title":"Scaling Neural Face Synthesis to High FPS and Low Latency by Neural Caching","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Face recognition and analysis","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; University of British Columbia Hospital","funders":"","keywords":"Computer science; Rendering (computer graphics); Image warping; Frame rate; Artificial neural network; Artificial intelligence; Teleconference; Latency (audio); Computer vision","score_opus":0.018505962578011338,"score_gpt":0.2771258627633585,"score_spread":0.25861990018534714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319300757","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2090426,0.0019031434,0.7378001,0.00071303174,0.00036718647,0.0001745193,0.00062153966,0.02012441,0.029253434],"genre_scores_gemma":[0.80992305,0.00043880707,0.1803547,0.00024789054,0.00005708206,0.00014410388,0.0006837985,0.0010535412,0.007097041],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997285,0.00002764262,0.0000144849055,0.000061841674,0.000114273316,0.000053114007],"domain_scores_gemma":[0.9994654,0.00022474375,0.000026054564,0.00013591337,0.00011870471,0.000029131985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032697027,0.0009662468,0.0004211393,0.00037535717,0.00032274527,0.00077071355,0.0012093316,0.0005982886,0.010075865],"category_scores_gemma":[0.0023513641,0.00027221846,0.0003305543,0.00044773606,0.0003650453,0.0014729847,0.0008986155,0.000871447,0.0017074505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008263713,0.0002811383,0.0020676637,0.0003210201,0.00012280383,0.0003667196,0.00021573453,0.28929698,0.15667535,0.01090383,0.017473143,0.5214492],"study_design_scores_gemma":[0.000057039702,0.00012793449,0.00054833415,0.000014465284,0.000026830388,0.00009955398,0.000038228452,0.94991285,0.039025962,0.0048595327,0.005270822,0.000018494837],"about_ca_topic_score_codex":0.009105247,"about_ca_topic_score_gemma":0.014796769,"teacher_disagreement_score":0.010075865,"about_ca_system_score_codex":0.0009885168,"about_ca_system_score_gemma":0.0009028191,"threshold_uncertainty_score":0.033707142},"labels":[],"label_agreement":null},{"id":"W4319300892","doi":"10.1109/wacv56688.2023.00278","title":"TeST: Test-time Self-Training under Distribution Shift","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Domain Adaptation and Few-Shot Learning","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":"Computer science; Test data; Artificial intelligence; Test (biology); Machine learning; Inference; Adaptation (eye); Data mining; Pattern recognition (psychology)","score_opus":0.02988191807964998,"score_gpt":0.2922269165930714,"score_spread":0.2623449985134214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319300892","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17959797,0.0016667745,0.75586975,0.0007852769,0.0007251613,0.00046211615,0.0014518455,0.05337493,0.006066104],"genre_scores_gemma":[0.7022183,0.00029863074,0.27759567,0.0013857291,0.000150105,0.00048711628,0.007940505,0.002461703,0.007462228],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872226,0.00033367562,0.0000692882,0.0004620901,0.000273072,0.00013949123],"domain_scores_gemma":[0.99630475,0.0013706976,0.00020099818,0.0013390279,0.0005978643,0.00018673408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027355028,0.0018457965,0.0010627046,0.00070352276,0.00051177666,0.00088020944,0.0035514864,0.0019169489,0.004324446],"category_scores_gemma":[0.010975115,0.0005877138,0.0009554469,0.0006698301,0.00088392256,0.0027062332,0.0025012873,0.0033402136,0.0025589995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011985736,0.000864257,0.009296238,0.00027432456,0.0004726443,0.0002887093,0.00017867246,0.277906,0.022040429,0.0027947486,0.037926868,0.64675844],"study_design_scores_gemma":[0.00006489519,0.00022400319,0.0011977982,0.000018199255,0.000030382163,0.00016653792,0.000046552785,0.9809327,0.011726691,0.0028242762,0.0027434365,0.000024490195],"about_ca_topic_score_codex":0.005789763,"about_ca_topic_score_gemma":0.0071300347,"teacher_disagreement_score":0.005789763,"about_ca_system_score_codex":0.00084411696,"about_ca_system_score_gemma":0.0013917397,"threshold_uncertainty_score":0.014466882},"labels":[],"label_agreement":null},{"id":"W4319300944","doi":"10.1109/wacv56688.2023.00266","title":"Bi-directional Frame Interpolation for Unsupervised Video Anomaly Detection","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Anomaly detection; Computer science; Interpolation (computer graphics); Optical flow; Motion interpolation; Artificial intelligence; Computer vision; Frame (networking); Anomaly (physics); Inter frame; Pattern recognition (psychology); Reference frame; Motion (physics); Block-matching algorithm; Video tracking; Image (mathematics); Video processing; Telecommunications","score_opus":0.02643759241011934,"score_gpt":0.30185371270497097,"score_spread":0.27541612029485163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319300944","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015763935,0.0001426341,0.9827429,0.000053221574,0.000020787596,0.000036039277,0.000060892628,0.00075276423,0.00042677345],"genre_scores_gemma":[0.39132246,0.0003323338,0.6052099,0.0000968276,0.00007493469,0.000118146774,0.00053092506,0.00013699097,0.0021775078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954706,0.000083844854,0.000022733033,0.00013761896,0.0001521713,0.000056565437],"domain_scores_gemma":[0.9993456,0.00018440718,0.00011228701,0.00011139109,0.00020613642,0.00004015234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071267923,0.0007453479,0.0006590522,0.0013167496,0.00035911662,0.00051500933,0.0012460283,0.0005941817,0.0010890254],"category_scores_gemma":[0.0020078975,0.00026327194,0.0005618993,0.00089992734,0.00046160232,0.0011899077,0.00079729385,0.0011333022,0.0003939504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004656683,0.00017275485,0.0033708427,0.00010142948,0.000066472334,0.00011172661,0.00013086777,0.13123067,0.06609703,0.009441311,0.0025992095,0.78621197],"study_design_scores_gemma":[0.000008238667,0.000074784315,0.00067293434,0.000006133509,0.000011674785,0.000062546875,0.000014464621,0.9775394,0.01743229,0.0029575953,0.001208656,0.000011235639],"about_ca_topic_score_codex":0.0045283632,"about_ca_topic_score_gemma":0.006430436,"teacher_disagreement_score":0.0045283632,"about_ca_system_score_codex":0.0005847134,"about_ca_system_score_gemma":0.0009195677,"threshold_uncertainty_score":0.009004056},"labels":[],"label_agreement":null},{"id":"W4319300975","doi":"10.1109/wacv56688.2023.00614","title":"HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":477,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Encoder; Segmentation; Artificial intelligence; Transformer; Convolutional neural network; Image segmentation; Pattern recognition (psychology); Scale-space segmentation; Computer vision; Voltage","score_opus":0.04367816882844694,"score_gpt":0.37248254271214737,"score_spread":0.32880437388370043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319300975","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062838914,0.00020092903,0.9886516,0.00009737062,0.00002591119,0.00005763574,0.00015204473,0.0038276678,0.0007029668],"genre_scores_gemma":[0.25166908,0.00052115496,0.74115974,0.00029642653,0.00006708544,0.00015636602,0.0012837233,0.0010884884,0.0037580242],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997501,0.00004242774,0.00001564388,0.000066484616,0.000091513175,0.000033888304],"domain_scores_gemma":[0.99970394,0.00010617664,0.000047087524,0.00007368007,0.000045530764,0.000023585077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006984437,0.0008606001,0.00057478755,0.0011732661,0.00022079427,0.00091669225,0.001210288,0.0007821267,0.0037284612],"category_scores_gemma":[0.0018521432,0.00045694795,0.00087125273,0.0008297327,0.0004378281,0.0015922883,0.0012075803,0.0009369971,0.0012486427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023544396,0.000096984106,0.0010132969,0.00019282386,0.00009367224,0.0001725672,0.00010803367,0.14314865,0.04572441,0.013485594,0.00922006,0.7865085],"study_design_scores_gemma":[0.000020134068,0.0000668748,0.0003443535,0.000014106162,0.000025619196,0.0001880834,0.000023096154,0.9619748,0.02405139,0.008865985,0.0044072876,0.000018367207],"about_ca_topic_score_codex":0.0024035994,"about_ca_topic_score_gemma":0.0037353067,"teacher_disagreement_score":0.0037284612,"about_ca_system_score_codex":0.00069804874,"about_ca_system_score_gemma":0.00091346755,"threshold_uncertainty_score":0.012472928},"labels":[],"label_agreement":null},{"id":"W4319301137","doi":"10.1109/wacv56688.2023.00128","title":"Seg&amp;Struct: The Interplay Between Part Segmentation and Structure Inference for 3D Shape Parsing","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"3D Shape Modeling and Analysis","field":"Engineering","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Inference; Segmentation; Computer science; Artificial intelligence; Parsing; Task (project management); Machine learning; Pattern recognition (psychology)","score_opus":0.030052725126645356,"score_gpt":0.32351511261528243,"score_spread":0.29346238748863707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319301137","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010262857,0.000509134,0.9549202,0.00036182097,0.000104077946,0.000121046585,0.0009328656,0.030394107,0.0023938932],"genre_scores_gemma":[0.13321589,0.00039642627,0.8522299,0.0007077655,0.00011255087,0.0001666438,0.004895132,0.0027200712,0.0055556567],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989483,0.00019016204,0.000044039498,0.000432493,0.00029500827,0.00009003742],"domain_scores_gemma":[0.99859697,0.0003863714,0.00008992228,0.0006175528,0.000203407,0.00010584153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018263054,0.0025571296,0.0016083615,0.0023795834,0.00082765473,0.0027314487,0.005039082,0.002923076,0.006957823],"category_scores_gemma":[0.0038272657,0.0015095185,0.00216168,0.0016510033,0.0018888373,0.005357934,0.003458059,0.002736955,0.0049650855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041038648,0.00027197105,0.0030204977,0.00032416751,0.00035626732,0.0002504029,0.00024822052,0.16285683,0.024673874,0.02176018,0.035891086,0.74993616],"study_design_scores_gemma":[0.000020506199,0.00010770877,0.0005422434,0.000026656231,0.00003812187,0.00013701008,0.00004132746,0.958002,0.011510387,0.022427456,0.0071156607,0.000030888346],"about_ca_topic_score_codex":0.0070853033,"about_ca_topic_score_gemma":0.02004604,"teacher_disagreement_score":0.0070853033,"about_ca_system_score_codex":0.0012809889,"about_ca_system_score_gemma":0.0023059687,"threshold_uncertainty_score":0.02327627},"labels":[],"label_agreement":null},{"id":"W4319302472","doi":"10.1109/wacv56688.2023.00004","title":"Table of Contents","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","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":"Office of Experimental Program to Stimulate Competitive Research; BC Cancer Agency; Universität Stuttgart; Sorbonne Université; Australian e-Health Research Centre; Deutsches Forschungszentrum für Künstliche Intelligenz; University of Tokyo; Università di Catania; Korea Advanced Institute of Science and Technology; Technische Universität München; Universität Ulm; Technische Universiteit Eindhoven; Technische Universität Darmstadt; Rheinische Friedrich-Wilhelms-Universität Bonn; University of British Columbia; University of California, San Diego; Seoul National University; Queensland University of Technology; Commonwealth Scientific and Industrial Research Organisation; Hanyang University; University of Southern California; École de technologie supérieure; Carnegie Mellon University; Microsoft Research; Dublin City University; National Science Foundation; Massachusetts General Hospital; Arkansas NSF EPSCoR; University of Queensland; University of Washington; Bristol-Myers Squibb","keywords":"Table (database); Computer science; Database","score_opus":0.0688548017813893,"score_gpt":0.26724590976974044,"score_spread":0.19839110798835113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319302472","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004717615,0.0038690176,0.002462981,0.003976362,0.011727415,0.00062990916,0.055758238,0.0027242352,0.91838014],"genre_scores_gemma":[0.001979653,0.0029532618,0.0015361826,0.0026673921,0.0025093975,0.00048924785,0.03558234,0.0010619744,0.9512206],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99928564,0.0000905623,0.00003574757,0.00011993609,0.00040057508,0.0000675935],"domain_scores_gemma":[0.9966539,0.0005836406,0.00013271865,0.00031972403,0.0017644777,0.00054554443],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006773317,0.0010470703,0.0008947797,0.0044286116,0.0017168999,0.0045973863,0.0014245367,0.0010603751,0.8740549],"category_scores_gemma":[0.00771822,0.0003148672,0.0005901446,0.003189674,0.0003563355,0.0019560012,0.0019830095,0.0012171384,0.8347996],"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":[0.000015413063,0.000020802507,0.00010985956,0.00013478399,0.000002978462,0.000016366737,0.000011203653,0.00006280657,0.000078443256,0.0010440759,0.95226175,0.046241425],"study_design_scores_gemma":[0.000006104652,0.00001153789,0.00024818964,0.00016318623,0.0000035829023,0.000029488107,0.000028500177,0.000034507488,0.000074842465,0.0009896122,0.99840564,0.0000050037665],"about_ca_topic_score_codex":0.005106185,"about_ca_topic_score_gemma":0.006597215,"teacher_disagreement_score":0.12594509,"about_ca_system_score_codex":0.0017611855,"about_ca_system_score_gemma":0.003281384,"threshold_uncertainty_score":0.1796453},"labels":[],"label_agreement":null}]}