{"id":"W4398268017","doi":"10.7910/dvn/pkjufn/7ceqae","title":"FCC2002.318.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; Meteorology; Ran; Atmospheric sciences; Environmental science; Climatology; Geology; 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":[0.001320933,0.00245527,0.001640602,0.003611803,0.0007358424,0.002942672,0.003744045,0.002628893,0.1458888],"category_scores_gemma":[0.007594889,0.0008950965,0.001439886,0.006572261,0.0005006624,0.001448035,0.001867924,0.00158869,0.1730277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00174651,"about_ca_system_score_gemma":0.002063537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03357138,"about_ca_topic_score_gemma":0.04330107,"domain_scores_codex":[0.9990489,0.0002322946,0.0001046523,0.0002794331,0.0001711165,0.000163503],"domain_scores_gemma":[0.9977826,0.0006151267,0.0002216212,0.0005879463,0.0004775322,0.0003150918],"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":[0.00004357787,0.000009151568,0.0002968289,0.0002704394,0.00002035325,0.000007206429,0.000006567532,0.0001962921,0.00002652474,0.0002693121,0.9979138,0.0009400009],"study_design_scores_gemma":[0.000506955,0.00002705801,0.002481152,0.0003415321,0.00003887951,0.0000493137,0.00004200258,0.0009649172,0.00023298,0.001829706,0.9934549,0.00003056895],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005375264,0.00005535214,0.00003549964,0.00007396135,0.00002186627,0.000005103177,0.9987218,0.0003559899,0.0006766965],"genre_scores_gemma":[0.0003635052,0.00005481402,0.0001333868,0.00008832039,0.00001281077,0.00003543256,0.9985971,0.00009757937,0.0006169183],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8541113,"threshold_uncertainty_score":0.4880465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01980468588633883,"score_gpt":0.2740081748733256,"score_spread":0.2542034889869868,"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."}}