{"id":"W4398398318","doi":"10.7910/dvn/pkjufn/xsshbs","title":"FCC2003.356.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; Remote sensing; Environmental science; Atmospheric sciences; Geology; Physics; Aerospace engineering; Magnetic field; Engineering","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":[0.001367312,0.002523152,0.001706729,0.003751031,0.0007657475,0.003083897,0.003725275,0.002725986,0.1495858],"category_scores_gemma":[0.008419643,0.0009409416,0.001602096,0.006894465,0.0005000317,0.001452432,0.001921825,0.001640375,0.1737761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001754663,"about_ca_system_score_gemma":0.00218235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03399348,"about_ca_topic_score_gemma":0.04413212,"domain_scores_codex":[0.9990454,0.0002275305,0.0001060887,0.0002841776,0.0001699446,0.000166743],"domain_scores_gemma":[0.9975865,0.0007346202,0.0002374136,0.0006284787,0.0004900304,0.0003229406],"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":[0.00004483767,0.000009096676,0.0003291097,0.0003147192,0.00002373127,0.000007740915,0.000007298046,0.0002003056,0.00002648588,0.0002743324,0.9977822,0.000980282],"study_design_scores_gemma":[0.0005968506,0.00002781743,0.002597511,0.0004201898,0.00004890938,0.0000552854,0.00004631345,0.0009930538,0.0002408247,0.002117694,0.9928215,0.00003405249],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005077942,0.00006229456,0.00003516013,0.00007825031,0.0000217416,0.000004961525,0.9987851,0.0003689089,0.0005929261],"genre_scores_gemma":[0.0003648709,0.00006848313,0.0001468339,0.0001024063,0.00001479281,0.0000393207,0.9985319,0.0001121834,0.0006191551],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8504143,"threshold_uncertainty_score":0.5004142,"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."}}