{"id":"W7007972904","doi":"","title":"Assessment of myocardial injury using magnetic resonance imaging: Pathophysiology of Cardiovascular Disease","year":2003,"lang":"en","type":"other","venue":"NPARC","topic":"Sustainability and Climate Change Governance","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pathophysiology; Disease; Magnetic resonance imaging; Cardiac magnetic resonance; Myocardial infarction","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001662942,0.0003708319,0.0005304716,0.002252795,0.0004165233,0.001299406,0.0009678095,0.001083312,0.06814862],"category_scores_gemma":[0.005521551,0.0001271182,0.0002684416,0.002102926,0.0003942836,0.0008705296,0.0005742657,0.00064743,0.01998617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006669558,"about_ca_system_score_gemma":0.001258748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00755658,"about_ca_topic_score_gemma":0.01967618,"domain_scores_codex":[0.9992682,0.0001506427,0.00007987178,0.00006471312,0.0003890872,0.00004742304],"domain_scores_gemma":[0.9961619,0.0007320037,0.0003394379,0.0003271167,0.002042488,0.0003969714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003512547,0.0002753491,0.01580133,0.0006014249,0.00002857741,0.0004388397,0.00009693065,0.0001316096,0.001584294,0.003054751,0.5964411,0.3811945],"study_design_scores_gemma":[0.0002001237,0.0005037434,0.1382111,0.001458009,0.0001028405,0.002659169,0.0002929592,0.0005286777,0.003879848,0.00629533,0.8458248,0.00004340289],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01725756,0.03739889,0.002948547,0.03354088,0.006087957,0.0004501408,0.009640634,0.0005174872,0.8921579],"genre_scores_gemma":[0.1266517,0.05428803,0.007904225,0.004411487,0.008017692,0.000380186,0.01076862,0.000412438,0.7871658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06814862,"threshold_uncertainty_score":0.2279798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01002246937835241,"score_gpt":0.2426872744301855,"score_spread":0.2326648050518331,"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."}}