{"id":"W4398346572","doi":"10.7910/dvn/pkjufn/ciqm3x","title":"FCC2002.199.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; Engineering; Magnetic field","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":[0.001253061,0.002643354,0.001641908,0.003568198,0.0008438026,0.003522283,0.003747304,0.003040725,0.1575515],"category_scores_gemma":[0.007642307,0.0009746552,0.001572077,0.006205869,0.0005188761,0.001627717,0.001930407,0.001794148,0.189848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001864058,"about_ca_system_score_gemma":0.002294201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.040533,"about_ca_topic_score_gemma":0.05348418,"domain_scores_codex":[0.9990816,0.0002042099,0.000106145,0.000258393,0.0001739722,0.0001757067],"domain_scores_gemma":[0.9977857,0.0006538197,0.0001870987,0.0005548703,0.0005250628,0.0002935246],"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":[0.00004194803,0.000009623464,0.0002623145,0.000299845,0.00001946631,0.000007957851,0.000007610407,0.0001755079,0.00003114698,0.0002941534,0.9979419,0.0009085223],"study_design_scores_gemma":[0.0005048584,0.00002508714,0.002009318,0.0003594535,0.00003403645,0.00004401847,0.0000444607,0.0008228486,0.0002520793,0.001705481,0.9941689,0.00002950169],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004457656,0.00005112614,0.00003218678,0.00007430755,0.00002442785,0.000004931976,0.9986311,0.000412547,0.0007248336],"genre_scores_gemma":[0.0003185248,0.00005546179,0.0001534941,0.0001014104,0.00001164584,0.00003404093,0.9985648,0.0001399067,0.0006207923],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8424485,"threshold_uncertainty_score":0.5270623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01967518898421252,"score_gpt":0.2736661666418012,"score_spread":0.2539909776575887,"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."}}