{"id":"W4398281463","doi":"10.7910/dvn/pkjufn/wxkxo5","title":"FCC2001.011.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":[0.001349695,0.002359709,0.001745305,0.003579565,0.0007562353,0.002860991,0.003712246,0.00266739,0.1463754],"category_scores_gemma":[0.009102005,0.0009039648,0.001506317,0.006439411,0.0004803091,0.001388735,0.001909559,0.001629566,0.1481806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001820831,"about_ca_system_score_gemma":0.002269275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03644196,"about_ca_topic_score_gemma":0.04838246,"domain_scores_codex":[0.9990336,0.0002408831,0.0001033065,0.0002917709,0.0001687222,0.0001618245],"domain_scores_gemma":[0.9973781,0.0008431263,0.0002756114,0.0006400417,0.0005230419,0.0003401694],"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":[0.00005007211,0.000008697776,0.0004008096,0.0003418337,0.0000260539,0.000007649657,0.000007237979,0.0002064845,0.00002318531,0.0002910563,0.9976242,0.00101273],"study_design_scores_gemma":[0.0006493336,0.00002726405,0.003085444,0.00047908,0.00005723426,0.0000552103,0.00004587255,0.0009765945,0.0002281959,0.002282981,0.9920774,0.00003531495],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005110655,0.00006457967,0.00003305416,0.00008409533,0.00002071582,0.000004748267,0.9989189,0.0002951493,0.000527588],"genre_scores_gemma":[0.0004372682,0.00007788791,0.0001621819,0.0001198389,0.0000177464,0.00004683838,0.9983607,0.0001087657,0.0006687393],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8536246,"threshold_uncertainty_score":0.4896746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02002011636998217,"score_gpt":0.2749888831853574,"score_spread":0.2549687668153752,"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."}}