{"id":"W2604578767","doi":"10.1109/lls.2016.2644647","title":"Multiscale Metabolic Modeling Approach for Predicting Blood Alcohol Concentration","year":2016,"lang":"en","type":"article","venue":"IEEE Life Sciences Letters","topic":"Alcohol Consumption and Health Effects","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Alcohol; Alcohol dehydrogenase; Ethanol; Ethanol metabolism; Alcohol consumption; Body weight; Blood alcohol; Medicine; Chemistry; Poison control; Biochemistry; Environmental health; Endocrinology; Injury prevention","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":[0.0001883126,0.0004828697,0.0004314071,0.0003281116,0.0002273631,0.0004563931,0.000456627,0.0007224697,0.001202029],"category_scores_gemma":[0.0005956641,0.0002440173,0.0007975139,0.0002509106,0.0001949029,0.0003470861,0.0004211597,0.0003924978,0.0001592846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003074105,"about_ca_system_score_gemma":0.0004405029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006891002,"about_ca_topic_score_gemma":0.003317743,"domain_scores_codex":[0.999944,0.00001957936,0.000002973727,0.00001438074,0.00001179561,0.000007333316],"domain_scores_gemma":[0.9999088,0.00004579789,0.00001613806,0.000005648603,0.00001520916,0.000008347823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001926787,0.00001277579,0.0008509384,0.00001527163,0.0000287446,0.00006261099,0.00001130794,0.9880416,0.002065754,0.00426486,0.0001844089,0.004442499],"study_design_scores_gemma":[7.78717e-7,0.000002688351,0.00006595122,4.759085e-7,0.000002366029,0.000003088468,7.959789e-7,0.9993885,0.00004284221,0.0004265868,0.00006495717,0.000001033372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07476801,0.0003985735,0.9197938,0.0004199744,0.00007113351,0.00002776722,0.0002071977,0.0002479233,0.004065459],"genre_scores_gemma":[0.9552401,0.0005814963,0.04080369,0.00009341722,0.00006673268,0.0001037463,0.0001905935,0.00004454275,0.002875755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006891002,"threshold_uncertainty_score":0.0137018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1379146484735566,"score_gpt":0.3672309638057449,"score_spread":0.2293163153321883,"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."}}