{"id":"W4398325059","doi":"10.7910/dvn/pkjufn/cn2qwm","title":"FCC2003.055.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; Meteorology; Environmental science; Atmospheric sciences; Geology; Geography; Physics; Computer science; Engineering; Magnetic field; Aerospace 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":[0.001430705,0.002412716,0.001675145,0.003588317,0.0007352494,0.002835402,0.003748356,0.00265041,0.1324653],"category_scores_gemma":[0.00891068,0.0008711009,0.001577264,0.006375802,0.000499616,0.001396444,0.001868468,0.001631159,0.1461921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001772829,"about_ca_system_score_gemma":0.002231814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03490381,"about_ca_topic_score_gemma":0.04571063,"domain_scores_codex":[0.9989856,0.00025287,0.0001092858,0.0003006659,0.0001803087,0.0001712328],"domain_scores_gemma":[0.9974892,0.0007405978,0.0002547508,0.0006599791,0.0005213327,0.0003342076],"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":[0.00004945231,0.000009393231,0.0003836355,0.0003089882,0.00002604808,0.000007975095,0.000007096431,0.0002165732,0.00002623493,0.0002715984,0.997678,0.001015068],"study_design_scores_gemma":[0.0006317763,0.00002963114,0.003113518,0.0004292956,0.00005505673,0.00006030156,0.00004887674,0.00111759,0.0002631308,0.002271371,0.9919432,0.00003627565],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005815993,0.00006239518,0.0000365976,0.00008295215,0.00002257251,0.000005309962,0.9988579,0.0003424629,0.0005317979],"genre_scores_gemma":[0.0004183525,0.00006687151,0.0001599531,0.0001069838,0.00001646704,0.00004330692,0.9984822,0.0001029497,0.0006029374],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8675348,"threshold_uncertainty_score":0.4431405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01973706749555086,"score_gpt":0.2748691842451595,"score_spread":0.2551321167496087,"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."}}