{"id":"W4398471407","doi":"10.7910/dvn/pkjufn/4qzinb","title":"FCC2002.295.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; Environmental science; Meteorology; Atmospheric sciences; Remote sensing; Climatology; Geography; Geology; Physics; 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":[0.001338152,0.002423395,0.001604909,0.00363537,0.0007257956,0.002880109,0.003697224,0.002623475,0.137558],"category_scores_gemma":[0.007785345,0.0008946835,0.00146682,0.006437297,0.0004950796,0.001427448,0.001836794,0.001621865,0.1578579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001733313,"about_ca_system_score_gemma":0.002041907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03259482,"about_ca_topic_score_gemma":0.04171332,"domain_scores_codex":[0.9990441,0.0002334688,0.0001068214,0.000277889,0.0001732267,0.0001644299],"domain_scores_gemma":[0.9977016,0.0006614039,0.0002307286,0.0006029644,0.0004841103,0.0003193101],"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":[0.00004678906,0.000009664515,0.0003243007,0.000293366,0.00002199477,0.000007808849,0.000007083413,0.000213857,0.00002768327,0.0002881292,0.9978119,0.0009475288],"study_design_scores_gemma":[0.0005472502,0.00002883631,0.002699371,0.0003637609,0.00004161856,0.00005436383,0.0000444336,0.001034235,0.0002500772,0.001945187,0.9929585,0.00003235442],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005789444,0.00005605476,0.0000366819,0.00007445343,0.00002209409,0.00000520817,0.9987279,0.0003653448,0.0006543261],"genre_scores_gemma":[0.0003833029,0.00005554527,0.0001401976,0.0000890019,0.00001297825,0.00003627173,0.9985953,0.0001002958,0.0005871756],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.862442,"threshold_uncertainty_score":0.4601774,"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."}}