{"id":"W4398480452","doi":"10.7910/dvn/pkjufn/grwblt","title":"FCC2003.343.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; Geography; 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":[0.001385894,0.002551291,0.001742052,0.003780749,0.0007623669,0.003070527,0.003777188,0.002769984,0.1413891],"category_scores_gemma":[0.008697955,0.0009442955,0.00162353,0.007061709,0.0005097883,0.001451321,0.001936906,0.001636181,0.1633366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001798864,"about_ca_system_score_gemma":0.002225162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03526736,"about_ca_topic_score_gemma":0.04515567,"domain_scores_codex":[0.9990152,0.0002362553,0.0001092919,0.0002918118,0.0001761767,0.0001711938],"domain_scores_gemma":[0.9975185,0.0007476594,0.0002526188,0.0006424014,0.0005052122,0.0003336003],"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":[0.00004702886,0.000009169347,0.0003637184,0.0003294446,0.00002551122,0.000007941981,0.000007383977,0.0002139878,0.00002661147,0.0002762914,0.9976827,0.001010253],"study_design_scores_gemma":[0.0006049313,0.00002881551,0.002778826,0.0004344579,0.00005269241,0.00005788111,0.00004705325,0.001051284,0.0002491615,0.002171672,0.9924875,0.0000356464],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005368291,0.000066117,0.00003583471,0.0000796684,0.00002172468,0.000005060157,0.998799,0.0003640413,0.0005748745],"genre_scores_gemma":[0.0003941213,0.00007216388,0.0001521833,0.0001066522,0.00001538935,0.00004095044,0.9985055,0.0001097758,0.0006032091],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8586109,"threshold_uncertainty_score":0.4729938,"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."}}