{"id":"W4398298262","doi":"10.7910/dvn/pkjufn/wlchtk","title":"FCC2003.068.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; Geography; Geology; 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":[0.001436736,0.002408191,0.001718402,0.003618435,0.0007559868,0.002961081,0.003804716,0.002670324,0.1488053],"category_scores_gemma":[0.009079806,0.0009009475,0.001572291,0.006528484,0.0005001251,0.001443645,0.001947508,0.001629479,0.1642061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001750332,"about_ca_system_score_gemma":0.002237886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03466241,"about_ca_topic_score_gemma":0.04526483,"domain_scores_codex":[0.9990004,0.0002481947,0.0001080756,0.0002986577,0.000174915,0.0001697632],"domain_scores_gemma":[0.997447,0.0007592417,0.0002562028,0.0006697093,0.0005277162,0.0003401068],"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":[0.0000464624,0.000008725424,0.0003586197,0.0002994449,0.00002437809,0.000007380558,0.000006866236,0.0001933397,0.00002372329,0.0002648281,0.9977791,0.0009871715],"study_design_scores_gemma":[0.0005981316,0.00002745403,0.00280256,0.0004205446,0.00005142606,0.00005365225,0.0000460716,0.0009784308,0.0002305824,0.002164381,0.9925925,0.00003435542],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005207013,0.00006129249,0.00003538107,0.00008447158,0.0000230601,0.000005116241,0.9988304,0.0003402412,0.0005679322],"genre_scores_gemma":[0.0004047459,0.00006890888,0.0001551636,0.0001122925,0.00001711201,0.00004418752,0.9984249,0.0001092336,0.0006634727],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8511947,"threshold_uncertainty_score":0.4978032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01973706749555086,"score_gpt":0.2748691842451595,"score_spread":0.2551321167496087,"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."}}