{"id":"W4398272074","doi":"10.7910/dvn/pkjufn/e08xp7","title":"FCC2002.097.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; Environmental science; Atmospheric sciences; Remote sensing; Geography; Physics; Engineering; Magnetic field; Aerospace 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":[0.001394596,0.002349898,0.001683545,0.003659064,0.0007481648,0.002925328,0.003727288,0.002650201,0.1465149],"category_scores_gemma":[0.008927136,0.0008844432,0.001531797,0.00646817,0.0004878585,0.001423104,0.001917885,0.001606305,0.1597678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001747268,"about_ca_system_score_gemma":0.002247911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03437437,"about_ca_topic_score_gemma":0.04461017,"domain_scores_codex":[0.9990057,0.0002465771,0.0001064055,0.0002969464,0.0001756436,0.0001687225],"domain_scores_gemma":[0.9974551,0.0007660709,0.000256244,0.0006532514,0.0005281379,0.0003411902],"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":[0.00004764079,0.00000900103,0.0003764252,0.0003006265,0.0000245267,0.000007581421,0.000006868094,0.0001963558,0.00002465258,0.0002739941,0.9977369,0.0009953897],"study_design_scores_gemma":[0.0005948868,0.00002810332,0.002943126,0.0004261895,0.00005126907,0.00005438356,0.00004634611,0.0009806668,0.0002351936,0.002131066,0.9924744,0.00003439185],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005338208,0.00006021326,0.00003450691,0.00008226709,0.00002208823,0.00000508024,0.9988336,0.0003269774,0.0005819729],"genre_scores_gemma":[0.000408613,0.00006727949,0.0001504378,0.0001098499,0.00001659966,0.00004346004,0.9984431,0.0001061579,0.0006546287],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8534851,"threshold_uncertainty_score":0.4901412,"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."}}