{"id":"W4398273873","doi":"10.7910/dvn/pkjufn/bktkdk","title":"FCC2003.245.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; Ran; Atmospheric sciences; Meteorology; Environmental science; Geology; Physics; Materials science; Computer 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":[0.001338074,0.002533903,0.001656327,0.003469004,0.0007278007,0.002960414,0.003768166,0.002657932,0.1382924],"category_scores_gemma":[0.007248325,0.0009211373,0.001580282,0.006295952,0.0005006233,0.001454771,0.001856053,0.001669878,0.166061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001735486,"about_ca_system_score_gemma":0.002042657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03286559,"about_ca_topic_score_gemma":0.04263718,"domain_scores_codex":[0.999061,0.000224496,0.0001059088,0.0002762213,0.0001676318,0.0001646595],"domain_scores_gemma":[0.9978737,0.0005928547,0.0002068677,0.000581201,0.0004545454,0.0002908291],"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":[0.00004685387,0.00001011326,0.0002978672,0.0002898673,0.00002274881,0.000007613874,0.000006887917,0.0002158291,0.0000297852,0.0002825609,0.9978417,0.0009480696],"study_design_scores_gemma":[0.0005892233,0.00002891833,0.002550614,0.0003556492,0.00004347221,0.0000538377,0.00004372697,0.00107031,0.0002691115,0.001991119,0.9929712,0.0000328025],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005452467,0.00005455223,0.00003679531,0.00007162098,0.00002273954,0.000005185575,0.9987154,0.0003941916,0.0006450081],"genre_scores_gemma":[0.000350875,0.00005452282,0.0001422053,0.00008540358,0.00001229704,0.00003451604,0.9986416,0.0001063585,0.000572185],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8617076,"threshold_uncertainty_score":0.4626342,"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."}}