{"id":"W4404403674","doi":"10.48550/arxiv.2411.02710","title":"Full Field Digital Mammography Dataset from a Population Screening Program","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mammography; Digital mammography; Field (mathematics); Population; Mammography screening; Medical physics; Computer science; Medicine; Environmental health; Mathematics; Breast cancer; Internal medicine; Cancer","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006180536,0.00133122,0.001005691,0.002457596,0.0005730379,0.001025205,0.002067103,0.001610751,0.01238888],"category_scores_gemma":[0.003074455,0.0003355597,0.0009845912,0.003604146,0.0003820862,0.0004293869,0.001085802,0.0009705792,0.01599351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001385421,"about_ca_system_score_gemma":0.001842926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06774329,"about_ca_topic_score_gemma":0.1119727,"domain_scores_codex":[0.9992757,0.0001102925,0.00007001976,0.0001976514,0.0002273925,0.0001188803],"domain_scores_gemma":[0.9988974,0.0002397903,0.00008805952,0.0002602835,0.0003824606,0.0001319314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004434195,0.0003324484,0.01788774,0.001021723,0.0001965959,0.0003972713,0.00007357329,0.002561055,0.0009875846,0.0005187222,0.9476814,0.02789858],"study_design_scores_gemma":[0.000667394,0.0002478888,0.1532546,0.0005638938,0.0002789608,0.001950865,0.0004313168,0.01435848,0.002846343,0.003307512,0.8219424,0.0001503054],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007471994,0.0005565903,0.0004857698,0.000374762,0.00005556915,0.00007406087,0.988096,0.000973193,0.001912077],"genre_scores_gemma":[0.007026698,0.0001808663,0.001036781,0.00009492467,0.00001977991,0.00006538253,0.9907093,0.00002762347,0.0008386128],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06774329,"threshold_uncertainty_score":0.134698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.135401718133563,"score_gpt":0.2664911893614945,"score_spread":0.1310894712279314,"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."}}