{"id":"W4398271819","doi":"10.7910/dvn/pkjufn/fsfcoa","title":"FCC2001.164.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; Materials 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":[0.00128843,0.002395826,0.001660676,0.003541963,0.0007212194,0.003077286,0.00356573,0.002664136,0.1501687],"category_scores_gemma":[0.007911688,0.0009206986,0.001407259,0.006784085,0.0004878392,0.001447365,0.00187782,0.001617361,0.1705945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001766311,"about_ca_system_score_gemma":0.00209602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03312246,"about_ca_topic_score_gemma":0.04343357,"domain_scores_codex":[0.9990772,0.0002219633,0.0001065166,0.0002717239,0.0001645427,0.0001580129],"domain_scores_gemma":[0.9977512,0.0006662742,0.0002299944,0.0005646962,0.000475874,0.0003118949],"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":[0.0000438646,0.000008735668,0.0003081453,0.0003019577,0.00002058781,0.000007252214,0.000007156904,0.0001917238,0.00002593099,0.0002912277,0.9978852,0.000908261],"study_design_scores_gemma":[0.0005391975,0.00002668599,0.002525296,0.0003866758,0.00004055714,0.00004949627,0.00004352474,0.0008618816,0.0002227025,0.001908079,0.9933656,0.00003032001],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005025343,0.00005762825,0.00003326063,0.00007449475,0.00002163961,0.00000476373,0.9987487,0.0003351526,0.0006741122],"genre_scores_gemma":[0.000372908,0.00006325941,0.0001341826,0.00009659016,0.00001326407,0.00003575142,0.9985398,0.0001092457,0.0006350076],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8498313,"threshold_uncertainty_score":0.5023643,"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."}}