{"id":"W7073581227","doi":"","title":"Machine learning in medical imaging second international workshop, MLMI 2011, held in conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011 ; proceedings","year":2011,"lang":"en","type":"article","venue":"","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Conjunction (astronomy); Medical imaging; Optical imaging; High-dynamic-range imaging","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":[0.004478229,0.001198839,0.001857863,0.001621251,0.0009047627,0.003305753,0.001607984,0.001779334,0.0129993],"category_scores_gemma":[0.003677576,0.0005479938,0.0007497686,0.001164425,0.001007423,0.00153804,0.001763971,0.002771138,0.006836528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001688355,"about_ca_system_score_gemma":0.002970466,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01091058,"about_ca_topic_score_gemma":0.03665695,"domain_scores_codex":[0.9990946,0.0002688871,0.00004194309,0.0001721393,0.0003089283,0.0001134846],"domain_scores_gemma":[0.9976428,0.0005134422,0.00005740601,0.0002464634,0.001074831,0.0004649127],"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":[0.0004230697,0.0002487077,0.0006628842,0.0003919498,0.0001246785,0.0001701674,0.0001520501,0.004059229,0.007579575,0.005284918,0.647931,0.3329718],"study_design_scores_gemma":[0.0001945407,0.0005525906,0.01032718,0.000429403,0.0002844945,0.001423424,0.0005039826,0.1423351,0.0263649,0.02796462,0.7894502,0.0001696927],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.03401023,0.112922,0.6810216,0.06588123,0.04291101,0.0006314546,0.007325338,0.008066513,0.04723062],"genre_scores_gemma":[0.114489,0.04853158,0.4090073,0.004763846,0.01874355,0.0004808323,0.01242957,0.002306981,0.3892474],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9890894,"threshold_uncertainty_score":0.04348695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02242580312123113,"score_gpt":0.1950376664746295,"score_spread":0.1726118633533984,"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."}}