{"id":"W2033142790","doi":"10.1167/13.9.554","title":"Resolving the individual layers of the human lateral geniculate nucleus using high-resolution structural MRI","year":2013,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Fetal and Pediatric Neurological Disorders","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Lateral geniculate nucleus; Visual cortex; Thalamus; Neuroimaging; Contrast (vision); Optics; Physics; Image resolution; Nuclear magnetic resonance; Resolution (logic); Signal-to-noise ratio (imaging); Materials science; Computer science; Artificial intelligence; Neuroscience; Biology","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":[0.0003781269,0.0003562762,0.0001564629,0.0005606731,0.0001568306,0.0006055179,0.00022318,0.0004858738,0.001490376],"category_scores_gemma":[0.0008245839,0.0003213272,0.0001436131,0.0002184896,0.0003330961,0.0008001717,0.0002934579,0.0001762551,0.0002937073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002328069,"about_ca_system_score_gemma":0.0002567145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003592257,"about_ca_topic_score_gemma":0.008367731,"domain_scores_codex":[0.9999182,0.0000180877,0.000006310744,0.00002817347,0.00001563267,0.00001354129],"domain_scores_gemma":[0.9998717,0.00004666929,0.00002552062,0.0000219691,0.00002045604,0.00001363359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000666925,0.00006273249,0.03230327,0.0004061351,0.0002111835,0.0006615898,0.0008037849,0.00406948,0.8597509,0.0006251316,0.0007014857,0.09973729],"study_design_scores_gemma":[0.0001654267,0.0006259971,0.7557777,0.0001815076,0.0004430034,0.008819539,0.001124804,0.02313783,0.1951937,0.007145714,0.007275944,0.0001088201],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9683419,0.001970618,0.02694593,0.0002527706,0.00001285501,0.000061652,0.0002646687,0.0001477232,0.002001787],"genre_scores_gemma":[0.9668884,0.001033721,0.03110751,0.0001129491,0.0000144005,0.00002846593,0.0001814109,0.00004398877,0.0005890541],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003592257,"threshold_uncertainty_score":0.007142663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02442683494910089,"score_gpt":0.2816095331087921,"score_spread":0.2571826981596912,"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."}}