{"id":"W4398633481","doi":"10.7910/dvn/pkjufn/3igvgt","title":"FCC2001.329.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; Meteorology; Environmental science; Atmospheric sciences; Physics; Computer science; Materials science","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":[0.00131597,0.002359318,0.001626732,0.003529803,0.0007169163,0.002878948,0.003637353,0.002561236,0.144164],"category_scores_gemma":[0.007652849,0.000888181,0.001397326,0.006587752,0.0004847142,0.001376976,0.001817128,0.001552446,0.1653392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001785311,"about_ca_system_score_gemma":0.002067516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03401019,"about_ca_topic_score_gemma":0.04394436,"domain_scores_codex":[0.999078,0.0002232903,0.0001037427,0.0002705063,0.0001645766,0.0001597868],"domain_scores_gemma":[0.9977466,0.0006498664,0.000232133,0.0005786474,0.0004741617,0.000318446],"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":[0.00004534053,0.000008882289,0.0003155374,0.0002852266,0.00002029314,0.00000711065,0.000006478262,0.0001984153,0.00002528177,0.0002689636,0.9978408,0.000977744],"study_design_scores_gemma":[0.0005235256,0.00002711557,0.002680326,0.0003750727,0.0000406926,0.00005065416,0.00004125744,0.0009583363,0.0002334593,0.001840443,0.9931982,0.00003097895],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005392712,0.00005799976,0.00003486501,0.00007574644,0.0000212816,0.000004932816,0.9987325,0.0003420973,0.0006766841],"genre_scores_gemma":[0.0003824563,0.00006085576,0.0001331535,0.00009273114,0.0000131855,0.00003558131,0.9985597,0.00009719141,0.0006251264],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.855836,"threshold_uncertainty_score":0.4822767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01976153797269264,"score_gpt":0.2759116896298448,"score_spread":0.2561501516571522,"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."}}