{"id":"W2113000721","doi":"10.1109/crv.2010.25","title":"Semi-Automatic Prediction of Landmarks on Human Models in Varying Poses","year":2010,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Landmark; Artificial intelligence; Computer science; Inference; Hidden Markov model; Probabilistic logic; Markov chain; Pattern recognition (psychology); Computer vision; Position (finance); Machine learning","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":[0.0007103182,0.001104908,0.001104544,0.001126572,0.0004426767,0.0006861679,0.001598419,0.00113602,0.0022259],"category_scores_gemma":[0.002922212,0.001221619,0.001147474,0.0009912447,0.0008776208,0.001068741,0.001245018,0.001323737,0.001334248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004839715,"about_ca_system_score_gemma":0.0008668982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00809107,"about_ca_topic_score_gemma":0.01686492,"domain_scores_codex":[0.9993848,0.0001624637,0.00002064147,0.0002315143,0.0001425831,0.00005802447],"domain_scores_gemma":[0.9985356,0.0007218523,0.0001786456,0.0003113178,0.0001759155,0.00007658315],"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":[0.000273545,0.00006520288,0.003114899,0.00005472236,0.00007766787,0.0001540566,0.00009692443,0.7959751,0.007425691,0.002160772,0.002395958,0.1882055],"study_design_scores_gemma":[0.000004772943,0.00001828421,0.0003548737,0.000003821472,0.00000434235,0.00003626027,0.000007592343,0.9960803,0.001173231,0.002092007,0.000217947,0.000006669503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03290004,0.0001493338,0.9628443,0.00008984353,0.00002410168,0.00004769784,0.0002704698,0.003108517,0.000565819],"genre_scores_gemma":[0.6647431,0.0002627013,0.3302455,0.0000968071,0.0000512615,0.0001644332,0.001514291,0.0004878204,0.002434143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00809107,"threshold_uncertainty_score":0.01608795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02017829649914693,"score_gpt":0.2340097795478399,"score_spread":0.213831483048693,"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."}}