{"id":"W4398280746","doi":"10.7910/dvn/pkjufn/ysouzm","title":"FCC2001.157.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); Meteorology; Environmental science; Atmospheric sciences; Remote sensing; Geology; Geography; Physics; 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":[0.001202947,0.002444777,0.001672987,0.00356647,0.0007220835,0.003058357,0.003508383,0.002629956,0.1670429],"category_scores_gemma":[0.007246812,0.000951358,0.00143473,0.006753924,0.0004823718,0.001436275,0.001896067,0.001580426,0.1919596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001735098,"about_ca_system_score_gemma":0.002053033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03208299,"about_ca_topic_score_gemma":0.04195772,"domain_scores_codex":[0.9990963,0.000210974,0.000104374,0.0002658654,0.0001602651,0.000162212],"domain_scores_gemma":[0.9978149,0.0006257709,0.0002177068,0.0005611255,0.0004720984,0.0003083615],"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":[0.00004500618,0.000009150887,0.0002921033,0.0002966179,0.00002002817,0.000007153411,0.00000709781,0.0001843895,0.00002846045,0.0002887883,0.9979043,0.0009169775],"study_design_scores_gemma":[0.0005272861,0.00002606177,0.00235193,0.0003536615,0.00003748413,0.00004570922,0.00004168371,0.0008038221,0.000238943,0.001792477,0.9937516,0.00002941409],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004708098,0.00004955104,0.00003172546,0.0000659364,0.00002050181,0.000004638207,0.9987198,0.0003478511,0.0007129915],"genre_scores_gemma":[0.0003551973,0.00005826377,0.0001300885,0.00009055743,0.00001237415,0.00003470744,0.9985183,0.0001206922,0.0006797788],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8329571,"threshold_uncertainty_score":0.5588142,"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."}}