{"id":"W4398470040","doi":"10.7910/dvn/pkjufn/rfk4py","title":"FCC2003.081.ran","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Cardiovascular Health and Disease Prevention","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Range (aeronautics); Earth's magnetic field; Ran; Environmental science; Meteorology; Atmospheric sciences; Remote sensing; Geology; Physics; Materials science; Computer science; Magnetic field","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":[0.001408805,0.002379872,0.001719832,0.003602263,0.0007506259,0.002923757,0.003756647,0.002656432,0.1448051],"category_scores_gemma":[0.009092002,0.000901753,0.001584733,0.006505015,0.000491604,0.00141659,0.001952712,0.001628558,0.1568849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001780423,"about_ca_system_score_gemma":0.002261608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03609063,"about_ca_topic_score_gemma":0.04743671,"domain_scores_codex":[0.9990061,0.0002445221,0.0001077839,0.0002956548,0.0001749732,0.0001710532],"domain_scores_gemma":[0.9974076,0.0007872817,0.0002674837,0.0006601014,0.0005352568,0.0003421916],"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":[0.00004765384,0.000008705067,0.0003841315,0.0003134012,0.00002538127,0.000007469336,0.000007008145,0.0001972151,0.00002397669,0.0002732265,0.9977313,0.0009804695],"study_design_scores_gemma":[0.0006246881,0.00002753204,0.003028434,0.0004492025,0.00005459924,0.00005468064,0.00004717408,0.0009762376,0.000232888,0.002207244,0.9922618,0.00003545982],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005081279,0.000060141,0.00003294424,0.00008112197,0.00002169062,0.000004896949,0.9989077,0.000307564,0.0005331145],"genre_scores_gemma":[0.0004028178,0.00006972255,0.0001530818,0.000112286,0.00001683637,0.0000447025,0.9984515,0.0001052394,0.000643783],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8551949,"threshold_uncertainty_score":0.4844213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01973706749555086,"score_gpt":0.2748691842451595,"score_spread":0.2551321167496087,"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."}}