{"id":"W4388283437","doi":"10.1093/oso/9780199212903.003.0015","title":"Computational anatomy: contour matching using EPDiff","year":2009,"lang":"en","type":"book-chapter","venue":"","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Toronto","funders":"","keywords":"Measure (data warehouse); Computer science; Matching (statistics); Task (project management); Anatomy; Statement (logic); Artificial intelligence; Range (aeronautics); Computer vision; Biology; Mathematics; Philosophy; Epistemology; Engineering; Data mining","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":[0.001364411,0.000532547,0.0008473231,0.002332206,0.0005074065,0.002197533,0.002754632,0.001397594,0.01538876],"category_scores_gemma":[0.006224781,0.0005197431,0.0006447054,0.002373877,0.001044827,0.002818005,0.003321037,0.001402288,0.003292745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006083046,"about_ca_system_score_gemma":0.0005920019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001585182,"about_ca_topic_score_gemma":0.001434749,"domain_scores_codex":[0.9991357,0.0001522865,0.00005739347,0.0002034209,0.000402627,0.00004864695],"domain_scores_gemma":[0.9985093,0.000498393,0.00007729565,0.0004981212,0.0003461188,0.00007067928],"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":[0.000187751,0.0000685113,0.001008782,0.0002012862,0.00004938832,0.0002070226,0.0001455554,0.09354555,0.0120487,0.118921,0.01200803,0.7616084],"study_design_scores_gemma":[0.00003236809,0.00003646768,0.0002660145,0.00002860673,0.0000133795,0.0002153469,0.00002615171,0.8785281,0.00642398,0.1022655,0.01214685,0.00001720047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004167609,0.0002269683,0.9908084,0.000161333,0.00007269636,0.0000389133,0.00008309352,0.001516211,0.002924732],"genre_scores_gemma":[0.1539505,0.0003323736,0.8387858,0.0001997257,0.00007693225,0.00009419584,0.000493562,0.0008701851,0.005196752],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01538876,"threshold_uncertainty_score":0.05148053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01840517538477068,"score_gpt":0.2584398955617196,"score_spread":0.2400347201769489,"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."}}