{"id":"W2006080132","doi":"10.1016/j.neuroimage.2005.07.008","title":"Neuroanatomical differences between mouse strains as shown by high-resolution 3D MRI","year":2005,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":127,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; University of Toronto; Hospital for Sick Children","funders":"Canada Foundation for Innovation; Canadian Institutes of Health Research; Ontario Innovation Trust; Burroughs Wellcome Fund","keywords":"Neuroanatomy; Magnetic resonance imaging; Hippocampus; Strain (injury); High resolution; Artificial intelligence; Standard deviation; Biology; Lateral ventricles; Metric (unit); Anatomy; Pattern recognition (psychology); Neuroscience; Computer science; Mathematics; Medicine; Radiology; Geology; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006450121,0.0002434597,0.0003155481,0.00009460867,0.0001476835,0.00004353285,0.0002345724,0.00008670305,0.0001415647],"category_scores_gemma":[0.00008149605,0.0002252884,0.000092064,0.000203272,0.0001786826,0.0001735527,0.00009605524,0.0005135864,0.0001416908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004972905,"about_ca_system_score_gemma":0.00003670519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003403618,"about_ca_topic_score_gemma":0.000002169937,"domain_scores_codex":[0.9983459,0.00005059152,0.0003232718,0.0005824402,0.0003131181,0.000384645],"domain_scores_gemma":[0.9989513,0.00007873266,0.00009774292,0.0005810268,0.00005089649,0.0002402712],"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":[0.0001368002,0.001284097,0.02688173,0.00008079241,0.00005577046,0.0001393435,0.0001224167,0.00004592288,0.7575565,0.006871928,0.1164089,0.09041581],"study_design_scores_gemma":[0.003822819,0.001526517,0.2967671,0.00008080956,0.0004021103,0.0003034436,0.00003233725,0.01010922,0.1676938,0.00256193,0.5153878,0.001312067],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9599964,0.00005292043,0.01875512,0.01698967,0.00003989929,0.0005867089,0.0001737937,0.0008745236,0.002530959],"genre_scores_gemma":[0.983023,0.0001405702,0.01157672,0.002612532,0.0003084065,0.00004615708,0.0001095102,0.00006015114,0.002122923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5898627,"threshold_uncertainty_score":0.9186993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04755365049794241,"score_gpt":0.330409585953324,"score_spread":0.2828559354553816,"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."}}