{"id":"W4398280111","doi":"10.7910/dvn/pkjufn/x4ewmb","title":"FCC2001.151.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); Ran; Environmental science; Meteorology; Remote sensing; Atmospheric sciences; Physics; Geology; 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":[0.001204815,0.002606343,0.001721017,0.003601763,0.0007757768,0.003274112,0.003573277,0.002748036,0.1663379],"category_scores_gemma":[0.007137083,0.0009618051,0.001487906,0.006597896,0.0004986459,0.001467775,0.001958816,0.001656892,0.1959735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001815833,"about_ca_system_score_gemma":0.002170336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03518192,"about_ca_topic_score_gemma":0.04740838,"domain_scores_codex":[0.9990973,0.0002025453,0.0001048828,0.0002634468,0.0001629502,0.0001688605],"domain_scores_gemma":[0.9978589,0.0006183464,0.0002068599,0.0005323403,0.0004788027,0.0003047345],"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":[0.00004289785,0.000009534534,0.0002650277,0.0002948513,0.00001880824,0.000007093313,0.000007179481,0.0001766659,0.00002947244,0.0002836981,0.9979742,0.0008906611],"study_design_scores_gemma":[0.0005589635,0.00002646419,0.002241881,0.0003687576,0.00003765688,0.00004500981,0.00004354578,0.0007990394,0.0002449694,0.001782004,0.9938211,0.00003057865],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004294211,0.00004947605,0.00002981093,0.00006480683,0.00002147934,0.000004581322,0.9987285,0.0003512974,0.0007071337],"genre_scores_gemma":[0.0003162233,0.00005687003,0.0001268078,0.0000907497,0.00001150307,0.00003288222,0.9985735,0.0001203432,0.0006711698],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8336621,"threshold_uncertainty_score":0.5564557,"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."}}