{"id":"W4398399748","doi":"10.7910/dvn/pkjufn/gtmsle","title":"FCC2001.219.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":"Earth's magnetic field; Range (aeronautics); Environmental science; Meteorology; Atmospheric sciences; Remote sensing; Physics; Geology; Magnetic field; Aerospace engineering; Engineering","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":[0.001277195,0.002403416,0.001616937,0.00356675,0.0007444449,0.002999094,0.003626303,0.002612558,0.153487],"category_scores_gemma":[0.007635497,0.0009185143,0.001444133,0.006695226,0.0004896214,0.001443441,0.001855486,0.001581328,0.1761014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001786691,"about_ca_system_score_gemma":0.002073151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03473797,"about_ca_topic_score_gemma":0.04553515,"domain_scores_codex":[0.9990726,0.0002220226,0.0001050986,0.000270669,0.0001662771,0.0001634595],"domain_scores_gemma":[0.9977557,0.0006537827,0.0002295193,0.0005769559,0.0004776396,0.0003063208],"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":[0.00004289986,0.00000895059,0.0003029637,0.000281182,0.00001973344,0.000007124133,0.000006648406,0.0001898846,0.00002434308,0.0002775634,0.9978943,0.0009443386],"study_design_scores_gemma":[0.0005100181,0.00002571388,0.00245835,0.0003603965,0.00003824377,0.00004839757,0.00004148014,0.0008683034,0.0002203719,0.001821291,0.9935777,0.00002983022],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005003264,0.0000548916,0.00003283762,0.00007430049,0.00002156894,0.000004803474,0.9987056,0.0003324024,0.0007235844],"genre_scores_gemma":[0.0003661409,0.00005874433,0.0001313716,0.0000936565,0.00001282856,0.0000352198,0.9985385,0.0001021748,0.0006613065],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.846513,"threshold_uncertainty_score":0.513465,"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."}}