{"id":"W4390837511","doi":"10.1016/j.ajt.2024.01.006","title":"Advancing mouse models for transplantation research","year":2024,"lang":"en","type":"article","venue":"American Journal of Transplantation","topic":"Organ Transplantation Techniques and Outcomes","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Institute of Allergy and Infectious Diseases; National Institute of Diabetes and Digestive and Kidney Diseases; Chinook Therapeutics; National Center for Advancing Translational Sciences; Icahn School of Medicine at Mount Sinai","keywords":"Medicine; Transplantation; Intensive care medicine; Computational biology; Surgery; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0132296,0.001825397,0.001616145,0.004205056,0.001280223,0.00441107,0.003177911,0.00330732,0.01153152],"category_scores_gemma":[0.006046843,0.0009333467,0.001559732,0.001405289,0.001715356,0.004670792,0.002379177,0.01065926,0.003596522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001243331,"about_ca_system_score_gemma":0.002155082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001165101,"about_ca_topic_score_gemma":0.002962233,"domain_scores_codex":[0.9943089,0.002656043,0.0005541738,0.0004570491,0.001515777,0.0005080734],"domain_scores_gemma":[0.9917349,0.00284153,0.001226757,0.001743802,0.001464007,0.0009890312],"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":[0.002541853,0.003134713,0.006018104,0.002944815,0.0004789282,0.001170265,0.0008438542,0.001931182,0.6343598,0.1632143,0.07562468,0.1077375],"study_design_scores_gemma":[0.001235056,0.004685231,0.008201206,0.003028905,0.001357059,0.004362327,0.001156105,0.006876921,0.2800545,0.06095397,0.6278101,0.000278582],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1887199,0.1095275,0.5234331,0.06219005,0.02017743,0.002699392,0.01925076,0.006496127,0.06750569],"genre_scores_gemma":[0.3697995,0.1222365,0.4169371,0.01676321,0.00459822,0.007743892,0.02024326,0.002483818,0.03919461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0132296,"threshold_uncertainty_score":0.0699656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03436818215937171,"score_gpt":0.384653093611787,"score_spread":0.3502849114524153,"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."}}