{"id":"W981799813","doi":"","title":"Future directions in Multiple Instance Learning","year":2013,"lang":"en","type":"article","venue":"Applied Computer Science","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University; University of Guelph","funders":"","keywords":"Computer science; Classifier (UML); Artificial intelligence; Set (abstract data type); Machine learning; Independence (probability theory); Context (archaeology); Training set; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003697237,0.0001278937,0.0001246418,0.0002688402,0.0003591735,0.0004649188,0.001490311,0.00004553358,0.000009698435],"category_scores_gemma":[0.00001572206,0.0001155946,0.00002788204,0.002492662,0.0002169198,0.001116266,0.0004058343,0.0002540326,0.000180433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001075969,"about_ca_system_score_gemma":0.0001170435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003783004,"about_ca_topic_score_gemma":0.000004883761,"domain_scores_codex":[0.9984146,0.00002458383,0.0002148048,0.0005904194,0.0003712099,0.0003844071],"domain_scores_gemma":[0.9991278,0.00006002518,0.00008011836,0.0004959497,0.0001298683,0.0001062309],"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":[0.000001046327,0.00006472946,0.001210154,0.000004976402,0.00000141115,0.000001821018,0.0008128951,0.00005035558,0.02345015,0.09566581,0.0001617759,0.8785749],"study_design_scores_gemma":[0.0006084827,0.0001084923,0.1362611,0.00003498466,0.000001647971,0.00002549814,0.0001185838,0.6734374,0.1011568,0.01708801,0.07035255,0.0008064079],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01491465,0.00006918638,0.9802495,0.0009539676,0.0003455609,0.0002842968,1.21791e-7,0.0005492811,0.002633466],"genre_scores_gemma":[0.7745505,0.00003277561,0.2247696,0.0004023938,0.00009210726,0.00008759066,4.03111e-7,0.000004639698,0.00006005876],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8777685,"threshold_uncertainty_score":0.4713809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01135650919014782,"score_gpt":0.22145462015459,"score_spread":0.2100981109644421,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}