{"id":"W4398470922","doi":"10.7910/dvn/pkjufn/1oomro","title":"FCC2002.120.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":[0.001350714,0.002637466,0.001716264,0.003570962,0.0007765682,0.003292009,0.00378662,0.002890495,0.1259336],"category_scores_gemma":[0.007333868,0.0008887397,0.001546495,0.006945542,0.0005068803,0.001554377,0.001926889,0.001685104,0.1592388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001824991,"about_ca_system_score_gemma":0.002224211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03525465,"about_ca_topic_score_gemma":0.05046039,"domain_scores_codex":[0.9990016,0.0002426549,0.0001148948,0.0002835606,0.0001813444,0.0001758815],"domain_scores_gemma":[0.9979123,0.0005733154,0.0002063288,0.0005399351,0.0004874015,0.0002807463],"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":[0.0000463272,0.00001104446,0.0003012331,0.0003448039,0.00002300743,0.000009056116,0.000008060947,0.0002146074,0.00003534442,0.0003027806,0.9977595,0.0009441988],"study_design_scores_gemma":[0.0005322273,0.00002895315,0.002392768,0.0003951725,0.00004210176,0.0000551021,0.0000471155,0.0009503965,0.0002674152,0.001723102,0.9935335,0.00003207799],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005678065,0.0000632657,0.00003443477,0.00007068023,0.0000233607,0.00000527571,0.9986761,0.0003882846,0.0006817549],"genre_scores_gemma":[0.0003176978,0.00005585815,0.0001371022,0.00008096778,0.00001049579,0.00003170336,0.9987724,0.00009663607,0.0004970224],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8740664,"threshold_uncertainty_score":0.42129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0198179097441933,"score_gpt":0.2741131898192298,"score_spread":0.2542952800750365,"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."}}