{"id":"W3112546813","doi":"10.1101/2020.12.04.412296","title":"Cardiac metabolic imaging using hyperpolarized [1-13C]lactate as a substrate","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Substrate (aquarium); Bicarbonate; Chemistry; In vivo; Hyperpolarization (physics); Metabolism; Biochemistry; Nuclear magnetic resonance spectroscopy; Biology; Stereochemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.0007860097,0.0004033415,0.0003743155,0.0003397171,0.0001771185,0.00113035,0.0004874866,0.0007324468,0.001137815],"category_scores_gemma":[0.0006079148,0.0002558144,0.0001870253,0.0004030794,0.0004138839,0.0006166803,0.0002852034,0.0006685462,0.0002872307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003670324,"about_ca_system_score_gemma":0.0003542958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006717524,"about_ca_topic_score_gemma":0.0008511425,"domain_scores_codex":[0.9997631,0.0001223036,0.000007015235,0.00003826003,0.00003154543,0.00003773114],"domain_scores_gemma":[0.9996719,0.0001045047,0.00006368751,0.00003628245,0.00008235342,0.00004131692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000431126,0.00003348662,0.001833072,0.0002231959,0.00002919869,0.0004236083,0.00004025482,0.001057609,0.982964,0.0004926515,0.0004233111,0.01204857],"study_design_scores_gemma":[0.00007809453,0.0008801603,0.009704398,0.0001171125,0.0001031718,0.002090973,0.000125513,0.01578289,0.964064,0.0005555343,0.006434565,0.00006347557],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8719485,0.009747523,0.1085329,0.001605362,0.0001813844,0.0001258682,0.0004117171,0.0004188079,0.007027891],"genre_scores_gemma":[0.9240398,0.00461017,0.06734133,0.0005960392,0.00009253783,0.00009485985,0.0005470435,0.000134001,0.002544278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001137815,"threshold_uncertainty_score":0.004156828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01971484901949859,"score_gpt":0.2581687335483149,"score_spread":0.2384538845288163,"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."}}