{"meta":{"query_hash":"e4b9fbbe8850","filters":{"venue":"2021 IEEE International Conference on Data Mining (ICDM)"},"cohort_total":2,"direct_labels_cover":0,"predictions_cover":2,"exported":2,"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/e4b9fbbe8850","api":"https://metacan.xera.ac/api/v1/cohort?venue=2021+IEEE+International+Conference+on+Data+Mining+%28ICDM%29"},"results":[{"id":"W4206960316","doi":"10.1109/icdm51629.2021.00128","title":"Thin Semantics Enhancement via High-Frequency Priori Rule for Thin Structures Segmentation","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Data Mining (ICDM)","topic":"Retinal Imaging and Analysis","field":"Medicine","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":"Western University","funders":"Research and Development","keywords":"Segmentation; Computer science; Artificial intelligence; Pattern recognition (psychology); A priori and a posteriori; Fuse (electrical); Block (permutation group theory); Redundancy (engineering); Feature (linguistics); Semantics (computer science); Mathematics; Engineering","score_opus":0.10649622353821031,"score_gpt":0.3836745338031497,"score_spread":0.2771783102649394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206960316","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":[0.042404555,0.0002863416,0.95367736,0.00017304372,0.00004355867,0.000051140898,0.00009382312,0.0010592624,0.0022109582],"genre_scores_gemma":[0.61842966,0.00047688803,0.37318555,0.0004738366,0.000089985944,0.00011903171,0.00057797856,0.00039413813,0.0062529203],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997004,0.000037362017,0.000019035848,0.00010878791,0.0000874082,0.00004706343],"domain_scores_gemma":[0.9995647,0.000116650146,0.00007287149,0.00009079136,0.00010342834,0.00005148959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005929574,0.00085623714,0.00078864006,0.00076925626,0.00033956763,0.0007762253,0.0011711234,0.0010885617,0.0015858262],"category_scores_gemma":[0.0014818226,0.00039497396,0.00086588075,0.00044222546,0.00077285554,0.0018794734,0.0011153149,0.0012166254,0.0005307333],"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":[0.00033489664,0.00023419409,0.0031204086,0.000223418,0.00014054921,0.00038388107,0.00025940704,0.27178207,0.18094891,0.021577802,0.0045222407,0.5164722],"study_design_scores_gemma":[0.000014612735,0.00009125734,0.0009427468,0.000016245456,0.00003884722,0.00014786517,0.00003121054,0.9623001,0.024719547,0.010158273,0.0015195842,0.000019712139],"about_ca_topic_score_codex":0.0020642467,"about_ca_topic_score_gemma":0.00361064,"teacher_disagreement_score":0.0020642467,"about_ca_system_score_codex":0.0005236906,"about_ca_system_score_gemma":0.0007305897,"threshold_uncertainty_score":0.005305052},"labels":[],"label_agreement":null},{"id":"W4206990816","doi":"10.1109/icdm51629.2021.00080","title":"Combining Ranking and Point-wise Losses for Training Deep Survival Analysis Models","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Data Mining (ICDM)","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Science and Engineering Research Council","keywords":"Ranking (information retrieval); Computer science; Event (particle physics); Survival analysis; Artificial intelligence; Machine learning; Statistics; Parametric statistics; Proportional hazards model; Regression; Time point; Survival function; Function (biology); Regression analysis; Data mining; Mathematics","score_opus":0.358133889439754,"score_gpt":0.39978414934896417,"score_spread":0.041650259909210174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206990816","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":[0.05680907,0.0008045946,0.9387899,0.00076383393,0.00007537663,0.000082546794,0.00026745975,0.0013835349,0.0010236416],"genre_scores_gemma":[0.8033224,0.0007701115,0.18839371,0.0007922459,0.0001441523,0.000422166,0.0016003305,0.00026730972,0.004287551],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988123,0.00046047274,0.00008540947,0.00022997217,0.00026617898,0.00014569516],"domain_scores_gemma":[0.995624,0.002945718,0.0003582158,0.0002586338,0.00063268084,0.00018078554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050749904,0.0018114496,0.0015164757,0.0010478261,0.0003675589,0.0012501844,0.0019681763,0.0016933719,0.001647565],"category_scores_gemma":[0.0094548,0.00047165787,0.00092142395,0.0007765574,0.00091753335,0.0019241744,0.0016504468,0.0027897353,0.00057385856],"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":[0.00018537749,0.00016915295,0.0032684286,0.000087676344,0.00006900696,0.00009303036,0.00006196378,0.8852841,0.0013894199,0.004857993,0.0027805364,0.10175328],"study_design_scores_gemma":[0.000004802603,0.00003400154,0.00012280504,0.0000056006343,0.0000056817994,0.000009847378,0.0000045600673,0.9966761,0.00032341084,0.002682325,0.00012675852,0.0000041667404],"about_ca_topic_score_codex":0.003318648,"about_ca_topic_score_gemma":0.004109073,"teacher_disagreement_score":0.0050749904,"about_ca_system_score_codex":0.0013241784,"about_ca_system_score_gemma":0.0014013221,"threshold_uncertainty_score":0.026839435},"labels":[],"label_agreement":null}]}