{"id":"W4398615005","doi":"10.7910/dvn/pkjufn/0k05en","title":"FCC2003.009.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; Environmental science; Meteorology; Remote sensing; 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":[0.001361981,0.002259958,0.001674748,0.003359531,0.000747723,0.002937406,0.003756179,0.002585658,0.1589542],"category_scores_gemma":[0.008563085,0.0008830779,0.00143999,0.006129145,0.000476373,0.001443365,0.00190242,0.00159896,0.172804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001767418,"about_ca_system_score_gemma":0.002130602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03412927,"about_ca_topic_score_gemma":0.0435914,"domain_scores_codex":[0.9990729,0.0002273212,0.00009862502,0.0002830237,0.0001599839,0.0001581425],"domain_scores_gemma":[0.997607,0.0007124484,0.000238305,0.0006305213,0.0004960979,0.000315527],"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":[0.00004645789,0.000008337427,0.0003423643,0.0002879162,0.00002207075,0.000006737838,0.000006452189,0.0001794497,0.00002343846,0.0002860994,0.9978097,0.0009810195],"study_design_scores_gemma":[0.0005801633,0.00002478433,0.002588592,0.0003917808,0.00004521008,0.00004826666,0.00004205083,0.000901403,0.0002283175,0.002131469,0.9929866,0.00003141135],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004720393,0.00005740755,0.00003573774,0.00008159047,0.00002142647,0.000004790365,0.9988176,0.0003305874,0.0006036565],"genre_scores_gemma":[0.0004157847,0.00006874148,0.0001610975,0.0001154992,0.0000170625,0.00004354785,0.9983541,0.0001136825,0.0007104549],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8410458,"threshold_uncertainty_score":0.5317547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01954226926405431,"score_gpt":0.2744679213394577,"score_spread":0.2549256520754034,"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."}}