{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":3,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":3,"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","author_layer_release":"2026-06-26"},"query_hash":"9f7c85226d5b","filters":{"venue":"2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)"}},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Jianglin Fu","is_ca":true},{"name":"Saeed Ranjbar Alvar","is_ca":true},{"name":"Ivan V. Bajić","is_ca":true},{"name":"Rodney G. Vaughan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03254290750963065,"gpt":0.2684122498171554,"spread":0.2358693423075247,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001043678,0.002800767,0.001707701,0.002320919,0.0008001953,0.00120311,0.002983727,0.00215984,0.008351967],"category_scores_gemma":[0.002543583,0.0007936127,0.001710931,0.001578847,0.0005911782,0.001087272,0.001783796,0.001670511,0.01141653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008130423,"about_ca_system_score_gemma":0.0009149148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01917243,"about_ca_topic_score_gemma":0.02242755,"domain_scores_codex":[0.9983644,0.000169198,0.00008632528,0.0005904913,0.0005047201,0.0002849119],"domain_scores_gemma":[0.9989467,0.0001581008,0.00006773342,0.0005265662,0.0002118363,0.0000890181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002354002,0.001385906,0.008881679,0.002175475,0.0006225023,0.0004893463,0.0001419764,0.0258362,0.0584776,0.0019012,0.4791168,0.4186174],"study_design_scores_gemma":[0.001057573,0.002492327,0.08113634,0.0005005006,0.0003899777,0.007169777,0.0005595183,0.3644425,0.2400346,0.01027132,0.2914519,0.0004936898],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2218952,0.004587746,0.2015122,0.0008959125,0.001427121,0.001784956,0.4963582,0.04890632,0.0226325],"genre_scores_gemma":[0.1543635,0.0007882038,0.1525355,0.0004207877,0.0001342732,0.0008968717,0.6802442,0.0007812436,0.009835382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01917243,"threshold_uncertainty_score":0.03812164,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Naimul Khan","is_ca":true},{"name":"Nabila Abraham","is_ca":true},{"name":"Marcia Hon","is_ca":true},{"name":"Ling Guan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01327248583506513,"gpt":0.2608572534375549,"spread":0.2475847676024898,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007794991,0.0009947361,0.0009466409,0.001118349,0.0005880068,0.002523627,0.00171603,0.001894737,0.002381635],"category_scores_gemma":[0.02547583,0.0002721175,0.0004687886,0.001563629,0.002339781,0.004135303,0.003176955,0.003159593,0.0006411136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001611651,"about_ca_system_score_gemma":0.0007863602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001519471,"about_ca_topic_score_gemma":0.0009574001,"domain_scores_codex":[0.9970132,0.001386479,0.00008580629,0.0004158248,0.0008829909,0.0002156812],"domain_scores_gemma":[0.9870722,0.009659602,0.000612548,0.001523794,0.0007741099,0.000357776],"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":[0.0007398581,0.0004948355,0.01029412,0.0004891633,0.0001481149,0.0005236008,0.0006217333,0.2261049,0.01201184,0.07535245,0.01094414,0.6622753],"study_design_scores_gemma":[0.00003351241,0.0002065785,0.003210451,0.00007197887,0.00001832305,0.0003064303,0.0001876631,0.8299323,0.0120858,0.1473885,0.006525691,0.00003283684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1194922,0.006998064,0.8465653,0.01159761,0.0003553007,0.0001900955,0.0003044728,0.002170136,0.01232676],"genre_scores_gemma":[0.835665,0.001890234,0.1564138,0.0008130464,0.0005910882,0.0001915514,0.0003270993,0.0002774298,0.003830798],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007794991,"threshold_uncertainty_score":0.04122436,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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)","authors":[{"name":"Jila Hosseinkhani","is_ca":true},{"name":"Chris Joslin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03595719900652511,"gpt":0.321767415896429,"spread":0.2858102168899039,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001240545,0.000718838,0.0007363124,0.001473927,0.0003859767,0.001050423,0.0007124307,0.0005471248,0.001767557],"category_scores_gemma":[0.008838336,0.0002830431,0.0006568663,0.0005256187,0.0005348297,0.001677692,0.0008480428,0.0006015549,0.0002298531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009259573,"about_ca_system_score_gemma":0.0006965549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004042199,"about_ca_topic_score_gemma":0.003980197,"domain_scores_codex":[0.9992682,0.0001382359,0.00004010101,0.0002050162,0.0002244077,0.0001239376],"domain_scores_gemma":[0.9977553,0.001067231,0.000273925,0.0002473066,0.000509875,0.00014631],"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":[0.00249268,0.0006614889,0.01936181,0.0007902657,0.0002393707,0.0004765623,0.0007228041,0.10686,0.2933511,0.01837315,0.002641357,0.5540294],"study_design_scores_gemma":[0.00008708538,0.0009221461,0.03288498,0.00004492829,0.0001354753,0.00033247,0.0001638723,0.9033678,0.04501518,0.01525645,0.001685997,0.0001035491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.316503,0.0004166265,0.6779153,0.0001450732,0.00005997771,0.0002988351,0.0002159853,0.0009082545,0.00353685],"genre_scores_gemma":[0.9193497,0.00007866853,0.07978228,0.00003051086,0.0000327487,0.00005685211,0.0001404736,0.00004655892,0.0004821937],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004042199,"threshold_uncertainty_score":0.008037388,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}