{"id":"W4398265080","doi":"10.7910/dvn/pkjufn/hgti7b","title":"FCC2003.059.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; Ran; Atmospheric sciences; Climatology; Environmental science; Geology; Geography; Physics; Computer science; 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":[0.001436529,0.002380697,0.001667005,0.003617104,0.0007432232,0.002876153,0.003752806,0.002635623,0.1382066],"category_scores_gemma":[0.009100117,0.0008835281,0.001565048,0.006522925,0.0004943595,0.00140908,0.001886617,0.001652478,0.1508687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001757816,"about_ca_system_score_gemma":0.002255488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03490989,"about_ca_topic_score_gemma":0.04535132,"domain_scores_codex":[0.998992,0.0002475128,0.0001093924,0.0003005653,0.0001798808,0.0001705673],"domain_scores_gemma":[0.9974295,0.000767389,0.0002607274,0.0006721015,0.000530015,0.0003401831],"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":[0.00004884507,0.000009112941,0.0003845256,0.0003052159,0.00002559507,0.000007823019,0.000007100856,0.0002071607,0.00002517753,0.0002776723,0.9976986,0.00100311],"study_design_scores_gemma":[0.0006152652,0.00002811133,0.003043061,0.000425427,0.00005343982,0.00005799322,0.00004747794,0.001043727,0.0002480526,0.002252599,0.9921496,0.0000353523],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000536781,0.00005883215,0.00003540152,0.00008022896,0.00002169849,0.000005058594,0.9988847,0.0003298223,0.0005306429],"genre_scores_gemma":[0.000405595,0.00006645775,0.0001573593,0.0001062493,0.00001615051,0.00004312655,0.9984934,0.0001049474,0.0006066575],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8617934,"threshold_uncertainty_score":0.4623473,"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."}}