{"id":"W4398335102","doi":"10.7910/dvn/pkjufn/camj6c","title":"FCC2002.308.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; Atmospheric sciences; Environmental science; Geomagnetic latitude; Remote sensing; Physics; Geology; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002179363,0.0003188405,0.0006860422,0.0001002161,0.00008321853,0.00004077305,0.0002278764,0.000302062,0.02064942],"category_scores_gemma":[0.0005695683,0.0003144029,0.0006529746,0.0001907664,0.00005166612,0.0001106738,0.0002342016,0.0005468399,0.2975013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001155379,"about_ca_system_score_gemma":0.0004486933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001746098,"about_ca_topic_score_gemma":0.00003025601,"domain_scores_codex":[0.9977643,0.00009930319,0.0003848532,0.0005905453,0.0007895118,0.0003714682],"domain_scores_gemma":[0.9973335,0.00003336951,0.0001337131,0.001570822,0.00006786823,0.0008607014],"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":[0.0005433888,0.0001233426,0.000006784659,0.001637378,0.0003157074,0.0009855819,0.000005517826,5.047501e-7,0.000009539636,0.000003297327,0.9951187,0.001250203],"study_design_scores_gemma":[0.002298328,0.00006220896,0.000347798,0.0002438878,0.001832485,0.000135716,0.00002404731,0.00001180976,0.000006432526,0.00000721886,0.9947677,0.0002624258],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000188102,0.0000202206,0.00007290511,0.00004065749,0.0006135785,0.0007152852,0.9980345,0.00009348257,0.0003905893],"genre_scores_gemma":[0.00002415096,0.001193391,0.0001886385,0.003117688,0.001151052,0.00004539443,0.9940204,0.00003190891,0.0002273838],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2768518,"threshold_uncertainty_score":0.9999308,"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."}}