{"id":"W4398471946","doi":"10.7910/dvn/pkjufn/wrlzke","title":"FCC2003.103.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; Atmospheric sciences; Meteorology; Physics; Materials science; Computer 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":[0.001272721,0.002551177,0.001723682,0.003342878,0.0007805374,0.003452557,0.003693734,0.002773502,0.1552068],"category_scores_gemma":[0.007654598,0.0009453465,0.001527022,0.006772236,0.0004971668,0.001562204,0.001974339,0.001683701,0.1849957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001802961,"about_ca_system_score_gemma":0.002165468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03600118,"about_ca_topic_score_gemma":0.04989487,"domain_scores_codex":[0.9990281,0.0002252832,0.0001155227,0.0002767484,0.0001764017,0.0001780219],"domain_scores_gemma":[0.9977963,0.0006116814,0.0002076694,0.0005926573,0.0004987643,0.0002929762],"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":[0.00004716629,0.000009140576,0.0002844252,0.0003142433,0.00002191717,0.00000798156,0.000007363307,0.0001777675,0.00002863848,0.0003009448,0.9978708,0.0009295714],"study_design_scores_gemma":[0.0005257155,0.00002516508,0.002128698,0.0003698987,0.00003793279,0.00004648482,0.00004065265,0.0007844987,0.0002395705,0.001803159,0.9939686,0.00002960077],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004328136,0.00005272143,0.00003233587,0.00006699465,0.0000224933,0.000004733191,0.998646,0.0003911702,0.0007402672],"genre_scores_gemma":[0.0003291411,0.0000574205,0.000138252,0.0000950199,0.0000120359,0.00003431522,0.9985619,0.0001310623,0.0006408845],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8447932,"threshold_uncertainty_score":0.5192184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01960973928789571,"score_gpt":0.2741421727985428,"score_spread":0.2545324335106471,"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."}}