{"id":"W4398620725","doi":"10.7910/dvn/pkjufn/ihbz5p","title":"FCC2001.121.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; Atmospheric sciences; Environmental science; Geomagnetic latitude; Remote sensing; Physics; Geography; Materials science; 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":[0.001275227,0.002567455,0.001726082,0.003514531,0.0007680166,0.00324101,0.003665355,0.002819951,0.1498302],"category_scores_gemma":[0.007306495,0.0009351609,0.001486003,0.006582057,0.0004876384,0.001471918,0.001956489,0.001649416,0.1748862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001898161,"about_ca_system_score_gemma":0.002186771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03637788,"about_ca_topic_score_gemma":0.05146206,"domain_scores_codex":[0.999056,0.0002200745,0.0001089549,0.0002733947,0.00016958,0.0001718836],"domain_scores_gemma":[0.9978945,0.0006163805,0.0002061858,0.0005235962,0.0004755428,0.0002837831],"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":[0.00004427386,0.000009915579,0.0002758374,0.0003362324,0.00002063407,0.000007989113,0.000007437829,0.0001951239,0.00003069927,0.000309416,0.9978412,0.0009210264],"study_design_scores_gemma":[0.0005492656,0.00002620757,0.00220449,0.0003913273,0.00003842104,0.00004561545,0.00004112144,0.0008277346,0.0002451513,0.001731703,0.9938688,0.00003024267],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004465715,0.00005432161,0.00003158192,0.00006640983,0.00002161504,0.000004865864,0.9987065,0.0003618915,0.0007082182],"genre_scores_gemma":[0.0003180342,0.00005613444,0.0001342414,0.00008558362,0.00001073829,0.00003257415,0.9986579,0.0001112991,0.0005934327],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8501698,"threshold_uncertainty_score":0.5012319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01975815841255807,"score_gpt":0.2758215371085426,"score_spread":0.2560633786959845,"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."}}