{"id":"W4398304972","doi":"10.7910/dvn/pkjufn/tmnyby","title":"FCC2002.022.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; Geography; Physics; Computer science; 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":[0.001380682,0.002494281,0.00173277,0.003717305,0.0007575214,0.002944037,0.003815755,0.002722164,0.1398823],"category_scores_gemma":[0.008741697,0.0008843666,0.001577399,0.00645239,0.0004962973,0.001443996,0.001940249,0.001652183,0.1542567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001756532,"about_ca_system_score_gemma":0.002256912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03515571,"about_ca_topic_score_gemma":0.04545285,"domain_scores_codex":[0.9990004,0.0002458094,0.0001038383,0.0003010451,0.0001791793,0.0001697474],"domain_scores_gemma":[0.9975401,0.0007249963,0.0002479392,0.0006399028,0.0005091354,0.0003380106],"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":[0.00004856911,0.000009337069,0.0003790288,0.0003069413,0.0000256249,0.000008128147,0.00000712739,0.0002133101,0.00002686762,0.0002740754,0.9976891,0.001011843],"study_design_scores_gemma":[0.0005864459,0.00002812635,0.002823253,0.0004175377,0.00005259176,0.00005720832,0.00004663381,0.001039095,0.0002456594,0.002162924,0.9925056,0.00003497204],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005607826,0.00006493836,0.0000361272,0.00008426246,0.00002280436,0.000005210626,0.9988417,0.0003461063,0.0005427814],"genre_scores_gemma":[0.0004029416,0.00006911489,0.0001594418,0.0001089804,0.00001661675,0.0000430737,0.9984803,0.0001069935,0.0006125704],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8601177,"threshold_uncertainty_score":0.4679529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01974833928877279,"score_gpt":0.2738160048007391,"score_spread":0.2540676655119663,"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."}}