{"id":"W4398467466","doi":"10.7910/dvn/pkjufn/ozoadv","title":"FCC2002.339.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; Remote sensing; Atmospheric sciences; Geology; Physics; Aerospace engineering; Magnetic field; 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":[0.001343758,0.002508447,0.001649914,0.003626382,0.0007330952,0.002925519,0.003802957,0.002631072,0.1449437],"category_scores_gemma":[0.007562235,0.0009053481,0.001479484,0.006463967,0.0005026445,0.001427172,0.001901119,0.001617186,0.1680185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00169744,"about_ca_system_score_gemma":0.002009019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03167323,"about_ca_topic_score_gemma":0.04039815,"domain_scores_codex":[0.9990528,0.0002323622,0.0001034719,0.0002796365,0.0001684804,0.0001631973],"domain_scores_gemma":[0.9978001,0.000618701,0.0002231159,0.0005946059,0.0004502313,0.000313231],"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":[0.00004783085,0.00000983624,0.0003294731,0.0002885461,0.00002258118,0.000007456118,0.000006836718,0.0002147237,0.00002745927,0.0002790001,0.9977757,0.0009905514],"study_design_scores_gemma":[0.0005587034,0.0000291637,0.00266041,0.0003598159,0.00004226988,0.00005188233,0.00004313306,0.001018085,0.0002458865,0.001940212,0.993018,0.00003249771],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000055265,0.00005613605,0.00003590987,0.00007470062,0.00002243902,0.000005200365,0.998716,0.0003702514,0.0006640429],"genre_scores_gemma":[0.0003846176,0.00005716705,0.0001400829,0.00009097016,0.00001386987,0.00003718899,0.9985681,0.0001029903,0.0006049979],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8550563,"threshold_uncertainty_score":0.484885,"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."}}