{"id":"W4398407147","doi":"10.7910/dvn/pkjufn/u9txff","title":"FCC2001.333.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; Remote sensing; Atmospheric sciences; Geology; Physics; Aerospace engineering; Magnetic field; Engineering","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":[0.001284531,0.00241173,0.001644147,0.003590563,0.0007275153,0.002951813,0.003669994,0.00256639,0.1533189],"category_scores_gemma":[0.007711474,0.0009118975,0.001418405,0.006640971,0.0004898352,0.001425866,0.001887727,0.001592839,0.172878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001766666,"about_ca_system_score_gemma":0.002067095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0337574,"about_ca_topic_score_gemma":0.04481983,"domain_scores_codex":[0.9990777,0.0002236393,0.000101664,0.0002713796,0.0001641129,0.0001614729],"domain_scores_gemma":[0.9977019,0.0006714904,0.0002373722,0.0005917462,0.0004755499,0.0003219162],"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":[0.00004422224,0.000008700429,0.0003120381,0.0002832415,0.00002038547,0.000007124704,0.000006621514,0.000196313,0.0000241781,0.0002829731,0.9978353,0.0009788671],"study_design_scores_gemma":[0.0005159451,0.00002602525,0.00248983,0.0003753199,0.00004005085,0.0000483287,0.00004112363,0.0009320441,0.0002233229,0.001932283,0.9933455,0.00003030179],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005028125,0.00005714394,0.00003458427,0.00007569446,0.00002154786,0.000004810215,0.9987156,0.0003526915,0.0006875985],"genre_scores_gemma":[0.0003775874,0.00006163362,0.0001372277,0.00009538931,0.00001362655,0.000036313,0.9985121,0.0001060894,0.0006600142],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8466811,"threshold_uncertainty_score":0.5129027,"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."}}