{"id":"W2438632701","doi":"10.1007/978-1-61779-219-9_31","title":"Mouse Phenotyping with MRI","year":2011,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Cancer, Hypoxia, and Metabolism","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Centre for Phenogenomics; Hospital for Sick Children","funders":"","keywords":"Magnetic resonance imaging; Human disease; Preclinical imaging; Ex vivo; Computer science; Selection (genetic algorithm); In vivo; Disease; Artificial intelligence; Pathology; Biology; Medicine; Radiology; Biotechnology","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":[0.003652457,0.001688451,0.001121265,0.005300341,0.001297107,0.001671332,0.001727977,0.001921741,0.01569082],"category_scores_gemma":[0.001727987,0.001105572,0.001353343,0.001800411,0.001223073,0.001337647,0.001773224,0.003707572,0.007791355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006632187,"about_ca_system_score_gemma":0.0006956752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006961653,"about_ca_topic_score_gemma":0.001749566,"domain_scores_codex":[0.9971672,0.0006696581,0.0003476537,0.0006080623,0.0009622701,0.0002450941],"domain_scores_gemma":[0.9978533,0.0004810482,0.0003631136,0.0006826402,0.0003637407,0.0002560042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007729597,0.0004487968,0.001557902,0.0009883576,0.0001170858,0.0006221805,0.0004531747,0.001643038,0.8601078,0.01575496,0.02295546,0.09457827],"study_design_scores_gemma":[0.0002056549,0.001227771,0.006802227,0.0005984787,0.0002815662,0.003871614,0.0001949954,0.005415479,0.5351416,0.007952268,0.4381104,0.000197839],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07287289,0.01140604,0.8300161,0.002651787,0.001536572,0.003373204,0.02131587,0.01490566,0.04192187],"genre_scores_gemma":[0.1262714,0.02591623,0.7358357,0.00233695,0.0005238075,0.01017869,0.02636402,0.006564203,0.06600902],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01569082,"threshold_uncertainty_score":0.05249107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02379015916268799,"score_gpt":0.3385683918761834,"score_spread":0.3147782327134954,"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."}}