{"id":"W4398280815","doi":"10.7910/dvn/pkjufn/hthufo","title":"FCC2003.065.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); Ran; Environmental science; Meteorology; Atmospheric sciences; Remote sensing; Physics; Geology; Materials science; Computer 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":[0.0002140356,0.0003239844,0.0006953554,0.0001577391,0.00008455876,0.00004027357,0.0002272305,0.0003147888,0.0193894],"category_scores_gemma":[0.0005708499,0.0003202813,0.000627499,0.0002271265,0.00005420482,0.0001099549,0.000223075,0.000563914,0.345275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001119672,"about_ca_system_score_gemma":0.0006365395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000251363,"about_ca_topic_score_gemma":0.00002314249,"domain_scores_codex":[0.9978805,0.0001037481,0.0003902145,0.0006022626,0.000643999,0.000379292],"domain_scores_gemma":[0.9972792,0.00002887527,0.0001361907,0.00159009,0.00008745777,0.0008781903],"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":[0.0004960037,0.0001225945,0.000005629889,0.001649092,0.0003256273,0.001000703,0.000005777054,4.759581e-7,0.000008892202,0.000004717421,0.9955573,0.0008231942],"study_design_scores_gemma":[0.002336893,0.00006210425,0.0003009331,0.0002530499,0.001874331,0.0001383774,0.00002504116,0.000009383344,0.000005647163,0.000008274872,0.9947193,0.0002667151],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000145543,0.00002230864,0.00007138349,0.00003999801,0.0006794182,0.0007505735,0.9978665,0.00009692421,0.0004583532],"genre_scores_gemma":[0.00001534438,0.001433527,0.0002018871,0.003417332,0.001112578,0.0000458215,0.9934591,0.00003235543,0.0002820405],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3258857,"threshold_uncertainty_score":0.9999249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01971484281753135,"score_gpt":0.2746593021223578,"score_spread":0.2549444593048265,"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."}}