{"id":"W4398318786","doi":"10.7910/dvn/pkjufn/3cdqdy","title":"FCC2001.337.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":"Earth's magnetic field; Range (aeronautics); Meteorology; Ran; Environmental science; Atmospheric sciences; Remote sensing; Geography; Geology; Physics; Computer science; Engineering; Aerospace engineering; 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":[0.001292705,0.002384977,0.001644909,0.003565147,0.0007203127,0.002948839,0.003621099,0.002539283,0.15763],"category_scores_gemma":[0.007535175,0.0009180445,0.001403947,0.006646019,0.0004825658,0.001400969,0.001865215,0.001562323,0.1786368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001733817,"about_ca_system_score_gemma":0.001996612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03310975,"about_ca_topic_score_gemma":0.04305931,"domain_scores_codex":[0.9990859,0.0002207289,0.0001007971,0.0002682867,0.0001624935,0.0001619268],"domain_scores_gemma":[0.9977698,0.0006424752,0.0002289363,0.0005756105,0.0004677052,0.000315435],"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":[0.00004444455,0.00000887055,0.0003084642,0.0002776826,0.00002037381,0.000006923693,0.000006559842,0.000193008,0.00002465332,0.0002756888,0.9978552,0.0009781756],"study_design_scores_gemma":[0.000521976,0.00002680359,0.002543183,0.0003645815,0.00003913857,0.00004776617,0.00004030336,0.0009000111,0.0002251217,0.001862135,0.9933988,0.00003022497],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005044724,0.00005526366,0.00003441293,0.00007296669,0.00002120586,0.000004803261,0.998708,0.0003477106,0.0007051636],"genre_scores_gemma":[0.0003778173,0.00005980811,0.0001347397,0.00009367808,0.0000137354,0.00003643505,0.9984981,0.0001071812,0.0006785493],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.84237,"threshold_uncertainty_score":0.5273249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01976153797269264,"score_gpt":0.2759116896298448,"score_spread":0.2561501516571522,"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."}}