{"id":"W4398402978","doi":"10.7910/dvn/pkjufn/5ctlwl","title":"FCC2003.138.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; Geography; Geology; Physics; 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":[0.001241655,0.002622108,0.001719293,0.003561604,0.000761974,0.003250459,0.003659215,0.002717361,0.1463097],"category_scores_gemma":[0.006820252,0.0009139761,0.001582912,0.006675714,0.0004857963,0.001470623,0.001870333,0.001651351,0.1819953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001785813,"about_ca_system_score_gemma":0.00215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03568453,"about_ca_topic_score_gemma":0.04935559,"domain_scores_codex":[0.9990574,0.000212835,0.0001127809,0.000275273,0.0001676804,0.0001741064],"domain_scores_gemma":[0.9979912,0.0005426666,0.0001907023,0.0005310944,0.0004741969,0.0002701466],"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":[0.00004649756,0.00001070404,0.0002815218,0.0003168983,0.00002226132,0.000008508309,0.000007375255,0.0001984796,0.00003503085,0.0002797652,0.9978678,0.0009249495],"study_design_scores_gemma":[0.0005646216,0.00002832319,0.00233236,0.0003595502,0.00004051557,0.0000516389,0.00004418464,0.0008675715,0.0002671596,0.001649728,0.9937631,0.00003125112],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005036514,0.0000513901,0.00003281741,0.00005878972,0.00002154675,0.000004772586,0.9987023,0.0003964144,0.0006816171],"genre_scores_gemma":[0.0002931522,0.00005021115,0.0001262195,0.00007110371,0.00001003652,0.00002916744,0.9987606,0.0001097076,0.000549857],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8536903,"threshold_uncertainty_score":0.4894546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197430599522316,"score_gpt":0.2749926882248495,"score_spread":0.2552496282726179,"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."}}