{"id":"W2985516850","doi":"10.1109/tim.2006.876399","title":"Evidential Mapping for Mobile Robots With Range Sensors","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Mobile robot; Computer science; Bayesian probability; Spurious relationship; Range (aeronautics); Artificial intelligence; Robot; Computer vision; Machine learning; Engineering","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":[0.0001000693,0.0001279747,0.0001030012,0.000117497,0.0001446761,0.00005205105,0.00002431678,0.00004096935,0.00002104861],"category_scores_gemma":[5.685404e-7,0.0001230653,0.00003869907,0.0001052971,0.00001944589,0.0001087291,1.423064e-7,0.00005404579,0.000003889861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000123016,"about_ca_system_score_gemma":0.00001285968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003599781,"about_ca_topic_score_gemma":0.0001258268,"domain_scores_codex":[0.9992378,0.00001520027,0.0001930162,0.0001441603,0.0002658032,0.0001439947],"domain_scores_gemma":[0.9997532,0.00001238716,0.00002697144,0.00007792861,0.00008478434,0.00004466634],"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":[0.00004155731,0.0000503146,0.00004368495,0.00007196495,0.00004079549,4.887647e-7,0.000121565,0.9744975,0.01114618,0.00004337419,0.000109711,0.01383288],"study_design_scores_gemma":[0.008463518,0.0007787765,0.002284617,0.0003608523,0.0002970349,0.00002163055,0.001219493,0.6196744,0.3618026,0.0001322858,0.004031181,0.0009336075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1514473,0.00003757457,0.8473208,0.0000392808,0.0003277785,0.0005384924,0.00001135703,0.0001092003,0.0001682694],"genre_scores_gemma":[0.9961323,0.00004480314,0.003466935,0.00003430238,0.00004268893,0.0001839614,0.00001013335,0.00002542683,0.000059504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.844685,"threshold_uncertainty_score":0.5018457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02240637737438209,"score_gpt":0.2118134014077635,"score_spread":0.1894070240333814,"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."}}