{"id":"W1969576961","doi":"10.1016/j.neuroimage.2004.12.052","title":"Cortical thickness analysis in autism with heat kernel smoothing","year":2005,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":331,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Mental Health; University of Wisconsin-Madison","keywords":"Smoothing; Geodesic; Heat kernel; Kernel (algebra); Mathematics; Kernel smoother; Euclidean distance; Noise (video); Computer science; Artificial intelligence; Pattern recognition (psychology); Kernel method; Mathematical analysis; Statistics; Combinatorics; Radial basis function kernel; Support vector machine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003553421,0.0001273989,0.0002091509,0.0002888159,0.0000622633,0.0001673956,0.000584527,0.00004790639,0.0001005935],"category_scores_gemma":[0.00008687751,0.0001068738,0.00005464045,0.001237597,0.00009511424,0.0007264903,0.000156831,0.0003702524,0.00004384877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004816814,"about_ca_system_score_gemma":0.00004317172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007797518,"about_ca_topic_score_gemma":0.00007075827,"domain_scores_codex":[0.9983702,0.0001669232,0.0002713401,0.0004534761,0.0004563413,0.0002817104],"domain_scores_gemma":[0.9991221,0.0001474938,0.00004090824,0.0005267113,0.00003025659,0.0001325336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001338553,0.002384166,0.1285929,0.0001544247,0.0004477465,0.005224267,0.0123616,0.006021319,0.2088921,0.03819308,0.006835493,0.590759],"study_design_scores_gemma":[0.001249499,0.0002273212,0.4719642,0.00005393811,0.0001199626,0.0001046241,0.00003886887,0.4872521,0.03688021,0.0005718433,0.0009289376,0.0006085083],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04098563,0.00002089299,0.9536896,0.003256627,0.00003748237,0.0001252659,7.857436e-7,0.0003389606,0.001544783],"genre_scores_gemma":[0.7676068,0.00000920399,0.2287777,0.003400442,0.00002104889,0.00001508197,0.000002185154,0.000009708358,0.0001578492],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7266212,"threshold_uncertainty_score":0.4358187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01527490330966017,"score_gpt":0.2802562558993492,"score_spread":0.264981352589689,"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."}}