{"id":"W4398280695","doi":"10.7910/dvn/28075/vdi1l8","title":"events.2017.20180122111453.tab","year":2018,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Event (particle physics); Computer science; Event data; Database; Physics; Data modeling","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006924549,0.002537651,0.001430202,0.00388636,0.0008469248,0.003278924,0.002738319,0.001980442,0.1801715],"category_scores_gemma":[0.003619006,0.0007089653,0.001126549,0.005532299,0.0005362457,0.001780444,0.002193422,0.001657426,0.2383109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001633204,"about_ca_system_score_gemma":0.001659564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01519808,"about_ca_topic_score_gemma":0.03138361,"domain_scores_codex":[0.9993615,0.00008070476,0.00008763293,0.0001867804,0.0001357544,0.0001475693],"domain_scores_gemma":[0.998718,0.0002874967,0.0001727859,0.000284711,0.0003075959,0.0002294094],"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":[0.00005198086,0.00001462018,0.0003999122,0.0002656266,0.000009490978,0.00001459449,0.00001124135,0.0001068258,0.00005054814,0.0002929638,0.9973148,0.001467344],"study_design_scores_gemma":[0.0002399032,0.00002102193,0.002286287,0.0001928235,0.00001682479,0.00007383379,0.00005855692,0.0003835991,0.0002935644,0.001130459,0.9952818,0.00002130839],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001257308,0.00007452857,0.00004422246,0.00009063551,0.00004751414,0.000008513707,0.9980667,0.0004255086,0.001116661],"genre_scores_gemma":[0.0003675251,0.00005987402,0.00009842341,0.00005012022,0.00001888774,0.00002849558,0.9982885,0.00007128806,0.001016937],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8198285,"threshold_uncertainty_score":0.6027336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01619207186779557,"score_gpt":0.2966626893548206,"score_spread":0.280470617487025,"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."}}