{"id":"W4398543264","doi":"10.7910/dvn/pkjufn/tndvhb","title":"FCC2001.074.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; Geology; Geography; 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":[0.001382727,0.00232841,0.001688251,0.003602273,0.000734882,0.002862643,0.003691501,0.002620399,0.142015],"category_scores_gemma":[0.009008429,0.0008779403,0.001514413,0.006512101,0.0004834655,0.001393403,0.001865501,0.001622741,0.1535422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001777534,"about_ca_system_score_gemma":0.002255045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03563588,"about_ca_topic_score_gemma":0.04686013,"domain_scores_codex":[0.999022,0.0002416525,0.0001045864,0.0002913688,0.0001733845,0.0001669666],"domain_scores_gemma":[0.9974233,0.0007941695,0.0002661891,0.0006468176,0.0005325866,0.0003368972],"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":[0.00004654582,0.000008849846,0.0003900472,0.0003048065,0.0000245395,0.000007395444,0.000006753926,0.0001994078,0.00002360326,0.0002700861,0.9977114,0.001006635],"study_design_scores_gemma":[0.0006060714,0.00002770964,0.003139735,0.0004493305,0.00005393728,0.00005582298,0.00004697938,0.0009954778,0.000238352,0.002165382,0.9921863,0.00003492803],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005429298,0.00006446271,0.0000340511,0.00008430798,0.0000218541,0.000004936452,0.9988686,0.0003120721,0.0005554489],"genre_scores_gemma":[0.0004220664,0.00007397623,0.0001541356,0.0001127348,0.00001719249,0.00004457189,0.9984056,0.0001032661,0.0006664157],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.857985,"threshold_uncertainty_score":0.4750875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01975139362270359,"score_gpt":0.2758061501033529,"score_spread":0.2560547564806493,"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."}}