{"id":"W4398624561","doi":"10.7910/dvn/pkjufn/on0xsx","title":"FCC2003.029.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; Ran; Environmental science; Atmospheric sciences; Meteorology; Geology; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00139661,0.002387187,0.001714642,0.003619989,0.0007517318,0.00293672,0.003831615,0.002656987,0.149309],"category_scores_gemma":[0.008960851,0.0009089941,0.001567737,0.006578297,0.0004889204,0.00142895,0.00194489,0.001653696,0.1600007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001772254,"about_ca_system_score_gemma":0.002248705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03529466,"about_ca_topic_score_gemma":0.04523414,"domain_scores_codex":[0.9990169,0.0002392303,0.0001073843,0.0002961926,0.0001717751,0.0001685184],"domain_scores_gemma":[0.9974694,0.0007600165,0.0002577466,0.0006593815,0.0005160434,0.000337392],"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":[0.00004860617,0.00000879548,0.0003753179,0.0003090836,0.00002511611,0.000007755634,0.000007067265,0.0001996997,0.00002464837,0.0002843316,0.9977156,0.0009940679],"study_design_scores_gemma":[0.0006047781,0.00002687648,0.002840413,0.000421111,0.00005184095,0.00005506192,0.00004509612,0.0009711687,0.0002332415,0.00222707,0.9924891,0.0000343735],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005049472,0.00005854095,0.0000350255,0.00007997303,0.00002154178,0.000004951049,0.9988798,0.0003292603,0.0005403823],"genre_scores_gemma":[0.0004037079,0.00006810972,0.0001557823,0.0001102445,0.00001645234,0.00004353808,0.9984642,0.0001098072,0.000628141],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.850691,"threshold_uncertainty_score":0.4994884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0196040650095547,"score_gpt":0.2746220335984836,"score_spread":0.2550179685889289,"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."}}