{"id":"W4398337531","doi":"10.7910/dvn/pkjufn/txpn1x","title":"FCC2002.064.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; Ran; 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":[0.001423915,0.002413489,0.001716062,0.003641358,0.0007518393,0.002919344,0.003806846,0.002686342,0.1431061],"category_scores_gemma":[0.00894803,0.0008822309,0.001557449,0.006424078,0.0004993327,0.001425458,0.001904958,0.00161105,0.1599405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001734469,"about_ca_system_score_gemma":0.002229848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03431037,"about_ca_topic_score_gemma":0.04423652,"domain_scores_codex":[0.9989955,0.0002518931,0.000105041,0.0003010873,0.0001773096,0.0001691389],"domain_scores_gemma":[0.9975051,0.0007386903,0.0002502232,0.0006532505,0.0005174259,0.0003353915],"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":[0.00004794318,0.000009232177,0.0003725123,0.0002997066,0.00002516291,0.000007713398,0.000006881879,0.0002036366,0.00002539773,0.000265297,0.9977298,0.001006755],"study_design_scores_gemma":[0.0006093031,0.00002926348,0.002923882,0.0004210651,0.00005307919,0.00005679348,0.00004715148,0.001038557,0.0002460727,0.002145757,0.9923937,0.00003528412],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005662449,0.00006439567,0.00003649132,0.00008515288,0.00002325237,0.000005268942,0.9988153,0.0003454006,0.0005682337],"genre_scores_gemma":[0.0004142874,0.00006927113,0.0001565689,0.0001100252,0.00001726106,0.00004403779,0.9984276,0.0001068555,0.0006541664],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.856894,"threshold_uncertainty_score":0.4787375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0198179097441933,"score_gpt":0.2741131898192298,"score_spread":0.2542952800750365,"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."}}