{"id":"W4398619287","doi":"10.7910/dvn/pkjufn/5e9don","title":"FCC2003.133.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; Geology; Geography; Physics; Computer science; Engineering; Aerospace engineering; 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":[0.001201411,0.002554156,0.001669944,0.003337583,0.0007518206,0.003263236,0.003567693,0.002639025,0.1512742],"category_scores_gemma":[0.006532494,0.0009063425,0.001479115,0.006465734,0.0004810269,0.001449289,0.001877186,0.001584656,0.1896068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001766131,"about_ca_system_score_gemma":0.002076185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03413748,"about_ca_topic_score_gemma":0.04833825,"domain_scores_codex":[0.9990927,0.0002011029,0.0001057025,0.0002663756,0.0001655929,0.0001685489],"domain_scores_gemma":[0.9981022,0.0005022976,0.0001806995,0.0005052261,0.0004490292,0.000260455],"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":[0.00004434667,0.00001020391,0.0002724417,0.0002942792,0.00002005016,0.000008107027,0.000006962815,0.0001872094,0.00003380916,0.0002746227,0.997905,0.0009429327],"study_design_scores_gemma":[0.0005428772,0.00002746322,0.002213363,0.0003360859,0.00003644269,0.00004905331,0.00004119837,0.0008450777,0.0002609584,0.001630255,0.9939879,0.00002929752],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005024166,0.000052371,0.00003306269,0.00006079664,0.00002168172,0.000004811274,0.9986175,0.000411515,0.0007479159],"genre_scores_gemma":[0.0003037459,0.00005074036,0.0001258881,0.00007733986,0.00001033179,0.0000294533,0.998689,0.0001142022,0.0005993596],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8487258,"threshold_uncertainty_score":0.5060627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197428473691247,"score_gpt":0.2749074484732426,"score_spread":0.2551646011041179,"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."}}