{"id":"W4398688684","doi":"10.7910/dvn/pkjufn/miotfv","title":"FCC2002.332.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; Atmospheric sciences; Meteorology; Environmental science; Geology; 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":[0.00132498,0.002558211,0.001659742,0.003741364,0.000766948,0.003020362,0.003708972,0.002775314,0.1392345],"category_scores_gemma":[0.008105257,0.0009137113,0.001573744,0.006770072,0.0004966183,0.001426835,0.001884937,0.001612534,0.1606249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001767276,"about_ca_system_score_gemma":0.002202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03541913,"about_ca_topic_score_gemma":0.04563314,"domain_scores_codex":[0.9990576,0.0002252673,0.000100076,0.0002803527,0.0001710251,0.0001657079],"domain_scores_gemma":[0.9976665,0.000698029,0.0002325316,0.0005990498,0.0004825651,0.0003213199],"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":[0.00004466735,0.000009364133,0.0003544682,0.0003130866,0.00002396292,0.000008001734,0.000007228344,0.0002132714,0.00002744304,0.0002737107,0.9977338,0.0009909624],"study_design_scores_gemma":[0.0005650708,0.00002872928,0.002753627,0.000419129,0.00004960983,0.0000568013,0.00004760377,0.001067935,0.0002494295,0.002076089,0.9926515,0.00003458611],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005568839,0.00006580308,0.00003533283,0.00008068199,0.00002202553,0.000005082058,0.9987661,0.0003686274,0.0006007201],"genre_scores_gemma":[0.0003912322,0.00006904061,0.0001494726,0.0001034917,0.00001473474,0.00003928938,0.9985237,0.0001061211,0.0006029855],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8607655,"threshold_uncertainty_score":0.4657859,"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."}}