{"id":"W2105708895","doi":"10.1109/crv.2011.28","title":"Feature Tracking Evaluation for Pose Estimation in Underwater Environments","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Robustness (evolution); Underwater; Frame (networking); BitTorrent tracker; Feature extraction; Feature (linguistics); Pose; Key frame; Feature matching; Feature tracking; Monocular; Pattern recognition (psychology); Eye tracking; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000119818,0.00005859769,0.00004886237,0.00005305795,0.00001584101,0.00001111036,0.00002529914,0.0000565455,0.0000670728],"category_scores_gemma":[0.000009472262,0.0000550024,0.00001532332,0.00003998188,0.000003994679,0.0001086757,0.000002344288,0.0000315501,0.00001125596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006923873,"about_ca_system_score_gemma":0.000002941776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004968041,"about_ca_topic_score_gemma":0.00001983796,"domain_scores_codex":[0.9996602,0.00001003439,0.00008038188,0.00007409204,0.00008511321,0.00009020082],"domain_scores_gemma":[0.9998901,0.000009508251,0.00001001875,0.00006518942,0.000009188557,0.00001598761],"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":[0.00000544173,0.00001732311,0.0004655379,0.00001545865,0.000006190401,2.784997e-7,0.000333995,0.9829714,0.003671824,0.0003977638,0.0002036046,0.01191121],"study_design_scores_gemma":[0.0003243645,0.00001419308,0.007231174,0.000008551416,0.00001058894,3.569048e-7,0.00002542898,0.9742622,0.01660615,0.001327185,0.0001185927,0.00007119813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06992546,0.00002253679,0.9278681,0.00004237014,0.0001241983,0.0003336591,0.000001171657,0.00004299544,0.00163955],"genre_scores_gemma":[0.9783927,0.00000491385,0.0213629,0.00002547969,0.00001252141,0.00002004934,0.00006127656,0.0000148061,0.0001053563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9084672,"threshold_uncertainty_score":0.2242933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04237218382689879,"score_gpt":0.2435749163750779,"score_spread":0.2012027325481791,"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."}}