{"id":"W4398296396","doi":"10.7910/dvn/pkjufn/fspmep","title":"FCC2003.120.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; Environmental science; Meteorology; Atmospheric sciences; Remote sensing; Climatology; Geography; Geology; Physics; Magnetic field; Materials science","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":[0.00138633,0.002678452,0.001761396,0.003522257,0.0007889043,0.003367022,0.003889964,0.002917523,0.130241],"category_scores_gemma":[0.007454011,0.0009129511,0.001591303,0.007043144,0.0005202093,0.001579331,0.001983697,0.001708919,0.1673566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001855946,"about_ca_system_score_gemma":0.002256032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03563308,"about_ca_topic_score_gemma":0.05122143,"domain_scores_codex":[0.9989833,0.0002443147,0.0001196395,0.0002881013,0.0001846542,0.0001798034],"domain_scores_gemma":[0.9978716,0.0005787755,0.0002069869,0.0005605749,0.0004976853,0.0002842617],"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":[0.00004609519,0.00001080573,0.0002874731,0.0003432546,0.00002283323,0.000008869872,0.0000080523,0.0002114829,0.00003495832,0.0002986543,0.9977942,0.0009334036],"study_design_scores_gemma":[0.000545192,0.00002858853,0.002302985,0.0003926418,0.00004178978,0.00005514883,0.00004655083,0.000954493,0.0002694028,0.001771939,0.9935591,0.00003225834],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005449613,0.00006202324,0.00003498372,0.00006947836,0.00002350668,0.000005267461,0.9986659,0.0004106114,0.0006736688],"genre_scores_gemma":[0.0003049508,0.00005513645,0.0001387053,0.00008133081,0.00001037555,0.000031285,0.9987828,0.0001013964,0.0004939259],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.869759,"threshold_uncertainty_score":0.4356995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197430599522316,"score_gpt":0.2749926882248495,"score_spread":0.2552496282726179,"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."}}