{"id":"W2598660286","doi":"10.15353/vsnl.v2i1.116","title":"Towards Global Localization Using Global Descriptors","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Viewpoints; Artificial intelligence; Computer science; Outlier; Representation (politics); Similarity (geometry); Matching (statistics); Variety (cybernetics); Object (grammar); Computer vision; Pattern recognition (psychology); Machine learning; Image (mathematics); Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0002014914,0.000113111,0.0001801246,0.00006929966,0.00006306196,0.0001219507,0.00006289953,0.00003471453,0.000004967415],"category_scores_gemma":[0.00003143611,0.00007771125,0.00005068323,0.0001725857,0.00003438244,0.0003228029,0.00001176778,0.00003513238,0.000002567211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002552547,"about_ca_system_score_gemma":0.00005525755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001303116,"about_ca_topic_score_gemma":3.800457e-7,"domain_scores_codex":[0.9989608,0.00004905037,0.0004539171,0.00007130206,0.0003465545,0.0001183892],"domain_scores_gemma":[0.999354,0.00002885619,0.0001440756,0.00004661113,0.0003234522,0.000103012],"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":[0.00001038328,0.00001135515,0.01149238,0.00003483311,0.00002384184,0.000007452968,0.00002334966,0.9731818,0.0002623354,0.002646117,0.001157607,0.01114857],"study_design_scores_gemma":[0.0005640474,0.00003095663,0.007310544,0.0003381892,0.00001856513,0.0003597073,0.00006310435,0.9879391,0.00002000607,0.002238204,0.001009597,0.0001079617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08722214,0.000847304,0.9104081,0.0001202021,0.001127382,0.00004941861,0.00001080922,0.00003168422,0.0001829801],"genre_scores_gemma":[0.9963131,0.00003300029,0.003419264,0.00004895975,0.000169024,1.889331e-7,0.000002591051,0.00001010973,0.000003790711],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9090909,"threshold_uncertainty_score":0.3168973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01085048171917385,"score_gpt":0.2563529122621193,"score_spread":0.2455024305429454,"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."}}