{"id":"W4398681486","doi":"10.7910/dvn/pkjufn/t2ldhv","title":"FCC2001.081.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; 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":[0.001381252,0.00233576,0.001714332,0.003643781,0.0007444261,0.002893189,0.003698375,0.002632865,0.1456467],"category_scores_gemma":[0.009146188,0.0008959754,0.001533471,0.006533924,0.0004801871,0.001407023,0.001920568,0.00161595,0.1537783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001806074,"about_ca_system_score_gemma":0.002262059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03633581,"about_ca_topic_score_gemma":0.04806862,"domain_scores_codex":[0.999018,0.0002423922,0.0001055372,0.000293341,0.0001734339,0.0001674092],"domain_scores_gemma":[0.9973581,0.0008271263,0.000276555,0.0006532146,0.0005407516,0.0003442531],"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":[0.00004737453,0.000008571701,0.0003920838,0.0003217096,0.00002507075,0.000007397771,0.000007013594,0.0001970499,0.00002305959,0.0002780842,0.9976884,0.00100409],"study_design_scores_gemma":[0.000610898,0.00002704181,0.00310675,0.0004707001,0.00005497728,0.00005421808,0.00004654648,0.0009490665,0.0002248385,0.002205357,0.9922145,0.00003506403],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000501983,0.00006236089,0.00003221349,0.00008218108,0.00002092858,0.000004745133,0.9989146,0.000292711,0.0005400049],"genre_scores_gemma":[0.0004107453,0.00007378031,0.0001522522,0.0001131995,0.00001699624,0.00004505255,0.9984239,0.000103353,0.0006606554],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8543532,"threshold_uncertainty_score":0.4872369,"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."}}