{"id":"W2003014378","doi":"10.1109/3dpvt.2006.34","title":"Automatic Locating of Anthropometric Landmarks on 3D Human Models","year":2006,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Landmark; Artificial intelligence; Computer science; Pairwise comparison; Inference; Pattern recognition (psychology); Computer vision; Bayesian network; Set (abstract data type); Markov random field; Probabilistic logic; Hidden Markov model; Node (physics); Markov chain; Image segmentation; Machine learning; Segmentation","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":[0.00007868734,0.00008450886,0.0001613164,0.0002813172,0.0000386927,0.00001202192,0.00007042824,0.00003786796,0.0001586627],"category_scores_gemma":[0.000005080506,0.00007274572,0.00005365409,0.0004225501,0.00001298208,0.0000427378,0.000007826839,0.00006077591,0.00001558867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002159673,"about_ca_system_score_gemma":0.000002916488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003292916,"about_ca_topic_score_gemma":0.00002863276,"domain_scores_codex":[0.9994311,0.000006923592,0.0002209699,0.00008563353,0.0001294258,0.0001259974],"domain_scores_gemma":[0.9997575,0.00002898888,0.00002279922,0.0001482371,0.0000229636,0.00001945661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[1.558725e-7,0.00003099412,0.0001397666,0.00004797609,0.00001870519,6.388622e-7,0.00002069519,0.9933477,0.000543457,0.0005221912,0.0003258577,0.005001834],"study_design_scores_gemma":[0.0000927967,0.00001851448,0.0001103162,0.00003076068,0.00001788911,3.423442e-7,0.00002149773,0.9973421,0.001573709,0.0007013406,0.000004249004,0.00008647185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7001585,0.0001393809,0.2354469,0.000008031267,0.00002592852,0.0000294816,0.00000168206,0.0002982499,0.06389187],"genre_scores_gemma":[0.9965239,0.00000652437,0.003125963,0.000005892889,0.00003367434,0.000002142042,0.000008060187,0.00001656994,0.0002772313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2963655,"threshold_uncertainty_score":0.2966484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01220367947234757,"score_gpt":0.2214735886671535,"score_spread":0.209269909194806,"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."}}