{"id":"W4398546223","doi":"10.7910/dvn/pkjufn/aiiljp","title":"FCC2003.331.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; Geography; 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":[0.001323535,0.00254253,0.001696339,0.003625955,0.000778626,0.003067032,0.003764432,0.002783308,0.1488197],"category_scores_gemma":[0.008097138,0.0009507046,0.001601579,0.006636748,0.0004905096,0.001407037,0.001944712,0.001619193,0.1685283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001809713,"about_ca_system_score_gemma":0.002201789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03615579,"about_ca_topic_score_gemma":0.04758878,"domain_scores_codex":[0.9990661,0.0002225518,0.0001020363,0.0002766365,0.0001656158,0.0001670537],"domain_scores_gemma":[0.9976231,0.0007284688,0.0002335418,0.000605204,0.0004903926,0.0003191103],"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":[0.00004430674,0.000008947393,0.0003385903,0.0003214112,0.00002365777,0.000007739986,0.000007395344,0.0002020435,0.00002647577,0.0002770899,0.9977778,0.0009646024],"study_design_scores_gemma":[0.0005932129,0.00002758454,0.002639361,0.0004245151,0.0000490995,0.00005310218,0.00004607539,0.001019856,0.000242199,0.002100449,0.9927705,0.0000340115],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005024753,0.00006081337,0.00003424966,0.00007821955,0.0000213347,0.000004956525,0.9987914,0.0003634625,0.0005952764],"genre_scores_gemma":[0.0003801183,0.00006720488,0.0001492222,0.0001068161,0.00001457531,0.00004008512,0.9984988,0.0001126959,0.0006304362],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8511803,"threshold_uncertainty_score":0.4978515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01974085318951302,"score_gpt":0.2749119874452859,"score_spread":0.2551711342557729,"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."}}