{"id":"W4398465859","doi":"10.7910/dvn/pkjufn/e7c2sw","title":"FCC2001.325.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":"Earth's magnetic field; Range (aeronautics); Meteorology; Environmental science; Atmospheric sciences; Remote sensing; Physics; Geology; 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":[0.0002152018,0.0003238602,0.000695418,0.0001595151,0.00008455378,0.00004048749,0.0002301813,0.0003148364,0.02352621],"category_scores_gemma":[0.0004958118,0.0003200334,0.0006622032,0.0002038788,0.00005409964,0.0001115682,0.000229994,0.0005653939,0.3456804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001125862,"about_ca_system_score_gemma":0.0005788433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002760959,"about_ca_topic_score_gemma":0.00003125698,"domain_scores_codex":[0.9978759,0.0001008521,0.000390663,0.0006010357,0.0006468178,0.0003846825],"domain_scores_gemma":[0.9972798,0.00003157633,0.0001361953,0.001585916,0.00007817247,0.0008884016],"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":[0.0005227792,0.0001253114,0.000004708665,0.001622187,0.000325921,0.001047841,0.000005739554,4.162028e-7,0.00001107736,0.000004826185,0.995566,0.0007632624],"study_design_scores_gemma":[0.002370094,0.00006290079,0.0002937549,0.0002541693,0.001874626,0.0001465683,0.0000251597,0.000008144298,0.000005437128,0.00001127595,0.9946812,0.0002666059],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002044198,0.00002085955,0.00005978805,0.00005131868,0.0006541901,0.0007410041,0.9979498,0.0001003484,0.0004022094],"genre_scores_gemma":[0.0000168347,0.001238376,0.0001812477,0.003322041,0.001193224,0.00004546977,0.9937301,0.00003270244,0.0002399756],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3221542,"threshold_uncertainty_score":0.9999252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01976153797269264,"score_gpt":0.2759116896298448,"score_spread":0.2561501516571522,"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."}}