{"meta":{"query_hash":"9f7c85226d5b","filters":{"venue":"2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"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/9f7c85226d5b","api":"https://metacan.xera.ac/api/v1/cohort?venue=2019+IEEE+Conference+on+Multimedia+Information+Processing+and+Retrieval+%28MIPR%29"},"results":[{"id":"W2914002589","doi":"10.1109/mipr.2019.00011","title":"FDDB-360: Face Detection in 360-Degree Fisheye Images","year":2019,"lang":"en","type":"preprint","venue":"2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)","topic":"Face recognition and analysis","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Degree (music); Computer science; Computer vision; Artificial intelligence; Face detection; Face (sociological concept); Facial recognition system; Object-class detection; Detector; Cover (algebra); Computer graphics (images); Feature extraction; Engineering","score_opus":0.032542907509630646,"score_gpt":0.2684122498171554,"score_spread":0.23586934230752474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914002589","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07893686,0.0008075667,0.90093994,0.0028815032,0.004119889,0.0017761869,0.00018670896,0.0008092683,0.009542087],"genre_scores_gemma":[0.99171716,0.001255979,0.005239087,0.0004364871,0.00010287285,0.000034329427,0.00016091623,0.000020400441,0.0010327657],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969386,0.00013373856,0.00095522084,0.0006820269,0.0007979484,0.0004924732],"domain_scores_gemma":[0.99750924,0.00014692094,0.00084952073,0.0005969133,0.0006823862,0.00021499422],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00076428935,0.00053616724,0.00064060616,0.000994042,0.00021735355,0.0015518571,0.0008212328,0.00068479934,0.0000430188],"category_scores_gemma":[0.00033345196,0.00048072694,0.00015356288,0.0006919698,0.00013916023,0.0026827925,0.0003513849,0.0013157718,0.0006361541],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007935395,0.00007083254,0.00017796972,0.00067293394,0.000034069224,0.000003193244,0.003333157,0.0019859725,0.00090038794,0.00003136731,0.00033175136,0.992379],"study_design_scores_gemma":[0.0012682177,0.000104017985,0.002033147,0.0012591153,0.000038010003,0.000009835002,0.00048648939,0.9785034,0.014228313,0.0005162791,0.0007305005,0.00082264823],"about_ca_topic_score_codex":0.0000997588,"about_ca_topic_score_gemma":0.00001839165,"teacher_disagreement_score":0.99155635,"about_ca_system_score_codex":0.00015690051,"about_ca_system_score_gemma":0.0005602636,"threshold_uncertainty_score":0.99976444},"labels":[],"label_agreement":null},{"id":"W2941877317","doi":"10.1109/mipr.2019.00041","title":"Saliency Priority Using Bottom-up Features for Static and Dynamic Scenes Without Cognitive Bias","year":2019,"lang":"en","type":"article","venue":"2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Salient; Computer vision; Human visual system model; Eye tracking; Video tracking; Robustness (evolution); Pattern recognition (psychology); Object (grammar); Image (mathematics)","score_opus":0.03595719900652511,"score_gpt":0.321767415896429,"score_spread":0.2858102168899039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941877317","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7502106,0.00010467766,0.2473735,0.00017539713,0.0010436114,0.000771766,0.000027743856,0.0001493803,0.00014333069],"genre_scores_gemma":[0.9906687,0.00010031395,0.008513873,0.0002564187,0.000027858905,0.000010772364,0.000033466127,0.000009818065,0.0003787869],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844325,0.000065382665,0.00042755742,0.00034652592,0.0004092023,0.00030811224],"domain_scores_gemma":[0.9985804,0.00014347806,0.00039476537,0.00015798896,0.00057919143,0.00014415244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050958426,0.0002457567,0.0002755821,0.00028038042,0.00035009353,0.000694977,0.00019279627,0.00017100197,0.0000064810265],"category_scores_gemma":[0.0002508456,0.00020277142,0.000045697856,0.0003168831,0.00012949633,0.002553057,0.00005207079,0.00026095373,0.000030808136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073739444,0.00014346461,0.004546515,0.0015473986,0.000056831894,0.0000010643145,0.01705929,0.0001512672,0.01242495,0.0015844101,0.000095002586,0.9616524],"study_design_scores_gemma":[0.0019671544,0.00039664848,0.0076089576,0.0004849262,0.000027981667,0.000023268807,0.00094529364,0.9840883,0.0034873385,0.00050712173,0.00008734139,0.0003756879],"about_ca_topic_score_codex":0.000023563078,"about_ca_topic_score_gemma":0.0000040412096,"teacher_disagreement_score":0.983937,"about_ca_system_score_codex":0.000046966597,"about_ca_system_score_gemma":0.00025514018,"threshold_uncertainty_score":0.8268779},"labels":[],"label_agreement":null},{"id":"W2963442459","doi":"10.1109/mipr.2019.00023","title":"Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond","year":2019,"lang":"en","type":"preprint","venue":"2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)","topic":"AI in cancer detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Transfer of learning; Artificial intelligence; Machine learning; Segmentation; Rendering (computer graphics); Volume rendering; Online machine learning; Image segmentation; Class (philosophy); Semi-supervised learning","score_opus":0.013272485835065125,"score_gpt":0.26085725343755495,"score_spread":0.24758476760248982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963442459","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09737776,0.0013500925,0.86350816,0.0032829451,0.008439673,0.0026353085,0.00014225273,0.0020132235,0.021250572],"genre_scores_gemma":[0.99240106,0.0024166703,0.002450067,0.00038447083,0.00023716738,0.000041732914,0.000295032,0.00004615959,0.0017276274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99606496,0.00033176504,0.00090897654,0.00097192347,0.001121965,0.00060040207],"domain_scores_gemma":[0.9974212,0.00039967988,0.0008091363,0.00041966722,0.00062075246,0.00032958598],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0009964113,0.0007310359,0.0007504503,0.0008091779,0.0005546595,0.0014109478,0.00070408767,0.0008683025,0.000046712415],"category_scores_gemma":[0.00054419244,0.0006440037,0.00012309744,0.00043794996,0.00039882117,0.002124386,0.00039193113,0.0049598664,0.00022917624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012489088,0.00020156434,0.001603478,0.0021333478,0.00019058055,0.000012867946,0.023059793,0.019687545,0.002105859,0.000435958,0.0009751722,0.94834495],"study_design_scores_gemma":[0.0018558727,0.0013495887,0.0007933621,0.0014181793,0.000045775643,0.000031356598,0.0005327225,0.9729275,0.0029551578,0.00022425836,0.016982518,0.0008836804],"about_ca_topic_score_codex":0.000055607463,"about_ca_topic_score_gemma":0.000001911505,"teacher_disagreement_score":0.95324,"about_ca_system_score_codex":0.00024096982,"about_ca_system_score_gemma":0.0006152341,"threshold_uncertainty_score":0.9996257},"labels":[],"label_agreement":null}]}