{"id":"W4398262713","doi":"10.7910/dvn/pkjufn/vfoe2i","title":"FCC2003.040.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; Atmospheric sciences; Environmental science; Meteorology; Remote sensing; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002181196,0.0003245268,0.0006993118,0.0001554539,0.00008523159,0.00004038338,0.0002275118,0.000308478,0.02121361],"category_scores_gemma":[0.0005670811,0.0003207437,0.0006274512,0.0002248747,0.00005432241,0.000110502,0.0002273542,0.0005547103,0.3376879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001159292,"about_ca_system_score_gemma":0.0006390171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002596701,"about_ca_topic_score_gemma":0.00003087719,"domain_scores_codex":[0.9978751,0.0001040675,0.0003912458,0.0006034762,0.0006460282,0.0003801101],"domain_scores_gemma":[0.9972738,0.00002837647,0.0001364536,0.001593825,0.00008769135,0.0008798743],"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":[0.0005042669,0.0001239637,0.00000597162,0.001641485,0.0003178293,0.0009910816,0.000005831066,4.462866e-7,0.000009073332,0.00000492851,0.995562,0.000833078],"study_design_scores_gemma":[0.002317504,0.00006331854,0.0003310035,0.0002462502,0.001829632,0.0001409066,0.00002412535,0.000009211802,0.00000565152,0.000008469048,0.9947568,0.0002671145],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000162775,0.00002162277,0.00007070372,0.0000412759,0.0006780246,0.0007538025,0.997914,0.00009701484,0.0004072384],"genre_scores_gemma":[0.00001589361,0.001320618,0.0002082969,0.003434388,0.001118013,0.00004590175,0.9935977,0.00003241393,0.0002267609],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3164743,"threshold_uncertainty_score":0.9999245,"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."}}