{"id":"W4398468402","doi":"10.7910/dvn/pkjufn/1pt2nq","title":"FCC2001.229.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; Magnetic field; Engineering; 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":[0.001265418,0.00255645,0.001644487,0.003550123,0.0007486781,0.003112257,0.003594644,0.002677914,0.1527493],"category_scores_gemma":[0.007236599,0.0009601873,0.001523762,0.006639194,0.0004825332,0.001439903,0.001917119,0.001628673,0.1817061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001761951,"about_ca_system_score_gemma":0.002150449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03570021,"about_ca_topic_score_gemma":0.04660374,"domain_scores_codex":[0.9990683,0.0002152912,0.0001101607,0.0002628586,0.0001718705,0.0001714728],"domain_scores_gemma":[0.9978104,0.0006294458,0.0002097977,0.0005562759,0.0004922629,0.000301893],"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":[0.00004333798,0.000009643276,0.000274859,0.0002929984,0.00002060739,0.00000752105,0.000006933422,0.0001961592,0.00002855291,0.0002722502,0.9979451,0.0009019977],"study_design_scores_gemma":[0.0005588412,0.00002793863,0.002381059,0.00037451,0.00003983108,0.0000482416,0.0000436701,0.0008930959,0.0002474512,0.001809663,0.9935439,0.00003175768],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000046306,0.00005043096,0.0000312958,0.00006624639,0.00002123546,0.000004714086,0.9987476,0.0003533048,0.0006788795],"genre_scores_gemma":[0.00031618,0.00005488221,0.0001266637,0.00008483404,0.00001130091,0.00003232746,0.9986558,0.0001095497,0.0006084978],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8472507,"threshold_uncertainty_score":0.5109974,"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."}}