{"id":"W4398692117","doi":"10.7910/dvn/pkjufn/ymgfjt","title":"FCC2003.231.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; Computer science; Magnetic field; Aerospace engineering; Engineering","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":[0.001281101,0.002524428,0.001629173,0.003471674,0.0007412003,0.002991276,0.003740184,0.002666006,0.1353683],"category_scores_gemma":[0.00723625,0.0009155497,0.001523867,0.006420676,0.0004999152,0.001439142,0.001876877,0.001647148,0.1637376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00182428,"about_ca_system_score_gemma":0.002146873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03582168,"about_ca_topic_score_gemma":0.04704159,"domain_scores_codex":[0.9990838,0.0002130466,0.0001051036,0.0002662252,0.0001680339,0.000163814],"domain_scores_gemma":[0.9978556,0.0006001581,0.000211882,0.0005699662,0.0004621501,0.0003002978],"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":[0.00004474444,0.00000979002,0.0003070767,0.0002864572,0.00002118146,0.000007605695,0.000006873493,0.0002128495,0.00002802691,0.0002933599,0.9978435,0.0009384174],"study_design_scores_gemma":[0.0005563429,0.00002707097,0.002579006,0.000354428,0.000040963,0.00005100647,0.0000434877,0.001013808,0.0002550012,0.001975815,0.9930711,0.0000319664],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005126242,0.00005295947,0.00003421288,0.00007161809,0.00002186661,0.000004939503,0.9987391,0.0003662886,0.0006578441],"genre_scores_gemma":[0.0003436743,0.00005499771,0.0001376995,0.00008694665,0.00001176324,0.00003306401,0.9986497,0.0001000411,0.0005821298],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8646317,"threshold_uncertainty_score":0.4528522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01973939644770946,"score_gpt":0.2748316633744504,"score_spread":0.255092266926741,"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."}}