{"id":"W4398272449","doi":"10.7910/dvn/pkjufn/zalxho","title":"FCC2001.117.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; Meteorology; Environmental science; Atmospheric sciences; Remote sensing; Geography; Physics; Materials 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":[0.001247594,0.002446892,0.001671399,0.003426138,0.000766042,0.003188281,0.003603071,0.002704885,0.1634713],"category_scores_gemma":[0.007178921,0.0009329999,0.001389215,0.006807415,0.0004819903,0.00146836,0.001906411,0.001567219,0.1918139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00181949,"about_ca_system_score_gemma":0.00209168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03423192,"about_ca_topic_score_gemma":0.04827378,"domain_scores_codex":[0.9990696,0.000221469,0.0001088209,0.0002681304,0.0001652371,0.000166782],"domain_scores_gemma":[0.9978656,0.0006118374,0.0002021139,0.000546209,0.0004812276,0.0002931164],"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":[0.00004184837,0.000009087354,0.000255923,0.0003052691,0.00001867291,0.000007516471,0.000007122536,0.0001751048,0.00002879438,0.0002973861,0.9979408,0.0009123787],"study_design_scores_gemma":[0.0004846546,0.00002409538,0.002035045,0.000350793,0.00003406086,0.00004290582,0.0000389941,0.0007434472,0.0002274936,0.001641932,0.9943487,0.00002794687],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004579424,0.00005449986,0.00003277996,0.00006792774,0.00002119404,0.00000492871,0.9985847,0.0003734391,0.0008148038],"genre_scores_gemma":[0.0003225208,0.00005757689,0.0001320092,0.00008619139,0.00001110223,0.00003353048,0.998601,0.0001137759,0.0006422839],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8365288,"threshold_uncertainty_score":0.5468658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01972595430774384,"score_gpt":0.2756942898891233,"score_spread":0.2559683355813794,"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."}}