{"id":"W1884191365","doi":"10.1109/im.1999.805352","title":"Estimating pose through local geometry","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Pose; Computer vision; 3D pose estimation; Computer science; Articulated body pose estimation; Range (aeronautics); Matching (statistics); Robotics; Tracking (education); Object (grammar); Pattern recognition (psychology); Robot; Mathematics; Engineering","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":[0.00004805554,0.00007530287,0.0000729052,0.00002836119,0.00003408189,0.00002190968,0.00003383722,0.0000457539,0.0002492544],"category_scores_gemma":[0.00003029396,0.0000708047,0.00002222809,0.0001629852,0.00001336726,0.0000719979,0.000003628536,0.00005823775,0.00008977352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002728731,"about_ca_system_score_gemma":0.000005497648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001040162,"about_ca_topic_score_gemma":0.00000241023,"domain_scores_codex":[0.9995728,0.00000863891,0.0001168702,0.00007688523,0.00007999044,0.0001448164],"domain_scores_gemma":[0.9998117,0.00002268203,0.000007268381,0.000107823,0.00001778607,0.00003272268],"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":[2.51824e-7,0.00000620931,0.0001165967,0.00001637196,0.000006231656,0.000002641452,0.00003288964,0.9774778,0.0002280589,0.01948394,0.001113189,0.001515823],"study_design_scores_gemma":[0.000115617,0.000009480665,0.00004532568,0.000007677242,0.000004403631,0.000005670884,0.00004876556,0.9895149,0.006556522,0.001227736,0.002353256,0.0001106455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01718044,0.00007003258,0.9319335,0.00001326092,0.0003665362,0.00003784075,4.739364e-7,0.0002211825,0.05017675],"genre_scores_gemma":[0.8853082,0.000004464133,0.114355,0.0001015149,0.00003800959,0.000001166174,0.000004629642,0.0000198895,0.0001670813],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8681278,"threshold_uncertainty_score":0.2887332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01036036568672118,"score_gpt":0.2150022177541159,"score_spread":0.2046418520673947,"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."}}