{"id":"W2141441007","doi":"10.1109/tbme.2008.923106","title":"A Novel 3-D Image-Based Morphological Method for Phenotypic Analysis","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Morphometrics; Shape analysis (program analysis); Landmark; Artificial intelligence; Image registration; Pattern recognition (psychology); Image processing; Orientation (vector space); Scale (ratio); Computer vision; Computer science; Image (mathematics); Voxel; Mathematics; Biology; Geometry; Cartography; Geography","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":[0.0005257725,0.0006851344,0.000589642,0.001603694,0.0003542051,0.001040698,0.001439814,0.0006464066,0.002810525],"category_scores_gemma":[0.001124167,0.0004872992,0.000766226,0.001367605,0.0006768248,0.0008830946,0.00114624,0.00117207,0.001614966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003995523,"about_ca_system_score_gemma":0.0006447752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000507608,"about_ca_topic_score_gemma":0.000755031,"domain_scores_codex":[0.9995787,0.000068482,0.00002867251,0.00007710484,0.0002259284,0.0000211207],"domain_scores_gemma":[0.9993597,0.0001661931,0.00007596841,0.0001909339,0.0001641328,0.00004309553],"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":[0.00008264346,0.00005943922,0.0006361431,0.0004641998,0.0000737626,0.0003705556,0.0001563091,0.02098961,0.4531235,0.03656514,0.005640534,0.4818382],"study_design_scores_gemma":[0.00004364705,0.0001907238,0.002930354,0.00006722661,0.00009223115,0.003752608,0.00008783435,0.723674,0.1589915,0.02534562,0.08461948,0.000204762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001078054,0.00005101664,0.9978426,0.00003281741,0.00002118009,0.00002159023,0.00004424656,0.0005194212,0.0003891207],"genre_scores_gemma":[0.01632762,0.0001823339,0.9822437,0.00002638212,0.00002127284,0.00009865563,0.0001242842,0.0001446195,0.000831183],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002810525,"threshold_uncertainty_score":0.009402156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04536351878749747,"score_gpt":0.3007374252170686,"score_spread":0.2553739064295711,"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."}}