{"id":"W4398315705","doi":"10.7910/dvn/pkjufn/byyaf1","title":"FCC2002.258.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; Environmental science; Meteorology; Atmospheric sciences; Geomagnetic latitude; Remote sensing; 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":[0.001288989,0.002465158,0.001636474,0.003572106,0.0007293594,0.002991816,0.003674017,0.002604384,0.1449313],"category_scores_gemma":[0.007367813,0.0009186119,0.001482288,0.006548699,0.0004861382,0.001426064,0.001880259,0.001595413,0.1751569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001698116,"about_ca_system_score_gemma":0.002035867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03364224,"about_ca_topic_score_gemma":0.04266068,"domain_scores_codex":[0.9990608,0.0002232383,0.000105231,0.0002743907,0.0001690844,0.0001671472],"domain_scores_gemma":[0.9978084,0.0006092288,0.0002189711,0.0005789061,0.0004741704,0.0003103215],"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":[0.00004482636,0.000009438067,0.0002950324,0.0002696755,0.00002073611,0.000007386352,0.000006676896,0.0001942484,0.00002725697,0.0002682095,0.9979531,0.0009035081],"study_design_scores_gemma":[0.0005489332,0.00002858395,0.002596888,0.0003515346,0.00004017653,0.00004966555,0.00004416251,0.0009437085,0.0002398242,0.001845469,0.9932795,0.00003142822],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005137828,0.00004952427,0.00003278625,0.00006942815,0.00002188952,0.000004831873,0.9987707,0.0003421433,0.0006573859],"genre_scores_gemma":[0.0003428456,0.00005131477,0.0001245607,0.00008319436,0.00001263554,0.00003333029,0.9986487,0.00009817829,0.0006052929],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8550687,"threshold_uncertainty_score":0.4848436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0198179097441933,"score_gpt":0.2741131898192298,"score_spread":0.2542952800750365,"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."}}