{"id":"W4398624207","doi":"10.7910/dvn/pkjufn/yizocd","title":"FCC2001.171.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; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001210085,0.002493603,0.001647961,0.003553407,0.000760334,0.003157213,0.003592116,0.002670479,0.1611245],"category_scores_gemma":[0.007264046,0.000953119,0.001444469,0.006454108,0.000491329,0.001422343,0.001930306,0.001639937,0.1818095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001763294,"about_ca_system_score_gemma":0.002102583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03438255,"about_ca_topic_score_gemma":0.04565484,"domain_scores_codex":[0.9991358,0.0002005003,0.00009806762,0.0002553831,0.0001533656,0.0001568647],"domain_scores_gemma":[0.9978371,0.0006444812,0.0002122408,0.0005433834,0.000453298,0.0003095014],"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":[0.00004459162,0.000009325719,0.0002834763,0.0003003017,0.00001994224,0.000007283902,0.000007269322,0.0001833515,0.00002736486,0.0002972629,0.9979163,0.000903541],"study_design_scores_gemma":[0.0005760669,0.00002651581,0.002326152,0.0003690296,0.00003920143,0.00004555927,0.0000434167,0.0008244116,0.000233786,0.001890815,0.9935947,0.00003030668],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004665475,0.00005146117,0.00003069197,0.00006853456,0.00002130356,0.000004710579,0.9987339,0.000348365,0.0006943332],"genre_scores_gemma":[0.0003496079,0.00005986895,0.0001338273,0.00009344101,0.00001262966,0.00003508449,0.9985248,0.0001187682,0.0006719927],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8388755,"threshold_uncertainty_score":0.5390152,"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."}}