{"id":"W4398633296","doi":"10.7910/dvn/pkjufn/5flqrh","title":"FCC2002.163.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":[0.001284377,0.002515035,0.001677692,0.003558111,0.0007308592,0.003084092,0.003661112,0.002657918,0.1468021],"category_scores_gemma":[0.007474674,0.0009210891,0.001455814,0.006657203,0.0005035338,0.001464505,0.001914329,0.001613745,0.1748733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001729665,"about_ca_system_score_gemma":0.002063311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03230395,"about_ca_topic_score_gemma":0.04205472,"domain_scores_codex":[0.9990771,0.0002199828,0.0001020143,0.0002751897,0.0001641818,0.0001613846],"domain_scores_gemma":[0.9978374,0.0005934277,0.0002179597,0.0005795433,0.0004546485,0.000317141],"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":[0.00004628062,0.000009287497,0.000309081,0.0002912116,0.00002107736,0.000007493321,0.000007045026,0.0001940278,0.00002809182,0.0002815838,0.9978671,0.0009377969],"study_design_scores_gemma":[0.0005231167,0.00002733251,0.002415644,0.0003524887,0.00003948039,0.00004928788,0.00004294712,0.0009065166,0.0002398086,0.001828403,0.9935447,0.00003016179],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005511938,0.00005865274,0.00003516633,0.00007464142,0.00002343564,0.000005083773,0.998666,0.0003805144,0.0007013875],"genre_scores_gemma":[0.0003765399,0.00005928657,0.0001348586,0.00009256709,0.00001343808,0.00003490786,0.9985494,0.0001111891,0.000627841],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8531979,"threshold_uncertainty_score":0.491102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0198179097441933,"score_gpt":0.2741131898192298,"score_spread":0.2542952800750365,"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."}}