{"id":"W2331269236","doi":"10.7210/jrsj.22.83","title":"Indoor Navigation based on an Inaccurate Map using Object Recognition","year":2004,"lang":"en","type":"article","venue":"Journal of the Robotics Society of Japan","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Pan African Materials Institute; Canadian Institute for Advanced Research","keywords":"Artificial intelligence; Computer vision; Mobile robot navigation; Mobile robot; Computer science; Robot; Path (computing); Object (grammar); Motion planning; Representation (politics); Desk; Dead reckoning; Robot control; Global Positioning System","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003446425,0.0001232656,0.0001910804,0.00004813842,0.000103655,0.00003403235,0.0001531836,0.0001114002,0.000003917399],"category_scores_gemma":[0.00002394938,0.00009869165,0.0002505564,0.000202865,0.000043362,0.0001789143,0.000009973722,0.0002485863,0.000001345467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002645017,"about_ca_system_score_gemma":0.00008358131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001077068,"about_ca_topic_score_gemma":0.000002075461,"domain_scores_codex":[0.9989943,0.00004720535,0.0004236759,0.00006930759,0.000314891,0.0001506103],"domain_scores_gemma":[0.9992124,0.00003812154,0.0003122967,0.0001619944,0.0002139877,0.00006114294],"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":[0.0000113713,0.0000629421,0.0003506728,0.00007815326,0.00003558913,3.898947e-7,0.0003437224,0.9830109,0.01567131,0.00003081195,0.00006134046,0.0003427973],"study_design_scores_gemma":[0.0008872538,0.0001706348,0.0007098329,0.0005337682,0.00008422435,0.00001389326,0.0002712755,0.9700002,0.02590407,0.00128143,0.000009589807,0.0001338306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7574801,0.00003328341,0.2413035,0.0004153109,0.0006025419,0.0001101273,0.000006720125,0.00002137383,0.00002704168],"genre_scores_gemma":[0.9577754,0.00002007947,0.04186026,0.0001369323,0.0001638544,2.583956e-7,0.00001122287,0.00002896352,0.000003051575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2002953,"threshold_uncertainty_score":0.4024529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02663472612089588,"score_gpt":0.2451092419935825,"score_spread":0.2184745158726867,"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."}}