{"id":"W4398293277","doi":"10.7910/dvn/pkjufn/g9accd","title":"FCC2003.215.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":"Earth's magnetic field; Range (aeronautics); Meteorology; Environmental science; Ran; Atmospheric sciences; Remote sensing; Climatology; Geography; Geology; Physics; Computer science; 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":[0.001374278,0.002525975,0.001625464,0.003678034,0.0007377284,0.00284927,0.0038327,0.002706189,0.1292056],"category_scores_gemma":[0.007825883,0.0009060243,0.001578599,0.006597186,0.0005213115,0.001442563,0.00186428,0.001686604,0.1526243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001835776,"about_ca_system_score_gemma":0.00218206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0354021,"about_ca_topic_score_gemma":0.04598366,"domain_scores_codex":[0.999022,0.000237166,0.0001116551,0.0002795503,0.0001809138,0.0001686837],"domain_scores_gemma":[0.997724,0.0006335761,0.0002308121,0.0006114131,0.0004861903,0.0003139185],"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":[0.00004537481,0.000009941501,0.0003228919,0.0002872188,0.00002249346,0.000007784399,0.000006767694,0.0002223442,0.00002768762,0.0002769249,0.997804,0.0009665117],"study_design_scores_gemma":[0.0005659875,0.00002873752,0.00275525,0.0003663272,0.00004388489,0.00005690826,0.00004522702,0.001152022,0.0002639378,0.002005497,0.9926825,0.00003380651],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005971458,0.00005859356,0.00003669171,0.00007913182,0.00002314455,0.000005420473,0.9987312,0.0003769124,0.0006292289],"genre_scores_gemma":[0.0003672668,0.00005816963,0.0001460949,0.00008927577,0.00001274266,0.00003626313,0.998632,0.0000945455,0.0005636233],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8707944,"threshold_uncertainty_score":0,"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."}}