{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001491127,0.001994031,0.0022011,0.005249929,0.0008451886,0.002413918,0.00211785,0.001592945,0.003491292],"category_scores_gemma":[0.003127975,0.0006535436,0.001019609,0.005715386,0.0008656473,0.003259501,0.005431377,0.001569103,0.005251426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006758049,"about_ca_system_score_gemma":0.001154679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00465272,"about_ca_topic_score_gemma":0.005661119,"domain_scores_codex":[0.9984622,0.0002187202,0.00007110131,0.000632485,0.0004465209,0.0001691094],"domain_scores_gemma":[0.9983917,0.000191131,0.000199957,0.0007282259,0.0004182807,0.00007075087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003243052,0.0002075393,0.003756723,0.0004031866,0.0001549776,0.0002386982,0.0003981535,0.02578255,0.03151011,0.01256237,0.02251558,0.9021458],"study_design_scores_gemma":[0.0002263309,0.0006191198,0.007068926,0.0002073551,0.0002237738,0.001180314,0.00132308,0.7801412,0.06979109,0.05402696,0.08501143,0.0001804889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01603889,0.0006924059,0.970345,0.0001284976,0.00009287801,0.00007601846,0.0008456205,0.009600036,0.002180722],"genre_scores_gemma":[0.2653867,0.001088671,0.7162799,0.0003080532,0.0001421801,0.0002546686,0.007882494,0.0010019,0.007655382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005249929,"threshold_uncertainty_score":0.01167953,"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."}}