{"id":"W4398322505","doi":"10.7910/dvn/pkjufn/0ogcn6","title":"FCC2001.312.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; Environmental science; Meteorology; Atmospheric sciences; Remote sensing; Geology; Geography; Physics; Computer science; 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":[0.001361695,0.002404934,0.001674656,0.003652506,0.0007399815,0.00292213,0.00377419,0.002626852,0.145502],"category_scores_gemma":[0.008156148,0.0008944384,0.001413104,0.006779852,0.0005001663,0.001441495,0.001878754,0.001600917,0.1665013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001820899,"about_ca_system_score_gemma":0.002132247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03349647,"about_ca_topic_score_gemma":0.04353386,"domain_scores_codex":[0.9990218,0.00024322,0.0001088078,0.0002872219,0.0001743616,0.0001646003],"domain_scores_gemma":[0.9976137,0.0006941422,0.0002488596,0.0006160057,0.0004978629,0.0003294249],"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":[0.00004518061,0.000008879933,0.0003154998,0.0002953607,0.00002063637,0.000007137841,0.000006636552,0.0002005649,0.00002431152,0.000287569,0.9978198,0.0009684434],"study_design_scores_gemma":[0.0005221087,0.00002686283,0.002584013,0.000390816,0.00004121076,0.00005040438,0.00004222072,0.0009477696,0.0002226949,0.001991259,0.9931496,0.00003106429],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005178872,0.00005886395,0.00003509688,0.00007715172,0.00002104104,0.000004948998,0.9987557,0.0003334231,0.0006620357],"genre_scores_gemma":[0.0003754388,0.00006170762,0.0001380767,0.00009426085,0.00001318903,0.00003698997,0.9985656,0.00009541108,0.0006194007],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.854498,"threshold_uncertainty_score":0.4867528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01975765016317573,"score_gpt":0.2758100237446309,"score_spread":0.2560523735814552,"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."}}