{"id":"W2773365015","doi":"10.1109/iros.2017.8202221","title":"Context-coherent scenes of objects for camera pose estimation","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Pose; Computer science; Robustness (evolution); Computer vision; Pairwise comparison; 3D pose estimation; Cognitive neuroscience of visual object recognition; Object (grammar); Feature (linguistics); Pattern recognition (psychology); Object detection; Focus (optics); Articulated body pose estimation; Feature extraction","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":[0.00003631675,0.00005466623,0.00008744391,0.00002314006,0.0000604079,0.00003322873,0.00006224994,0.00003183919,0.00001866382],"category_scores_gemma":[0.00004869011,0.0000496485,0.00002828004,0.00001102155,0.00001558218,0.00006957435,0.000005523208,0.00001610499,0.000004253513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001277217,"about_ca_system_score_gemma":0.000006527522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006227548,"about_ca_topic_score_gemma":0.0000696065,"domain_scores_codex":[0.9997085,0.000002659831,0.0001105789,0.00005381463,0.00005124087,0.00007318894],"domain_scores_gemma":[0.9996979,0.00002061226,0.00003523426,0.0001694369,0.00005436587,0.00002248797],"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":[0.00001416232,0.00002719452,0.0008586139,0.0002173317,0.00003421888,5.148216e-7,0.000180312,0.906378,0.01186931,0.01131233,0.001709699,0.06739827],"study_design_scores_gemma":[0.0002929799,0.00002728726,0.001386239,0.0000240523,0.000009010259,2.805346e-7,0.00003060618,0.9389346,0.058728,0.0003341747,0.0001679789,0.00006478378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1626192,0.00009390702,0.8318531,0.0000993032,0.0004757616,0.000332277,0.000008998049,0.00009590373,0.004421603],"genre_scores_gemma":[0.9936761,0.000009748946,0.006096326,0.00001663733,0.00003027456,0.000006811146,0.00001302487,0.00001202576,0.0001390883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8310569,"threshold_uncertainty_score":0.2024607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01892232001809723,"score_gpt":0.2535823009641742,"score_spread":0.2346599809460769,"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."}}