{"id":"W2164363236","doi":"10.1109/imtc.2005.1604562","title":"Evidential Mapping for Mobile Robots with Range Sensors","year":2006,"lang":"en","type":"article","venue":"2005 IEEE Instrumentationand Measurement Technology Conference Proceedings","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Mobile robot; Computer science; Spurious relationship; Range (aeronautics); Artificial intelligence; Robot; Bayesian probability; Mobile mapping; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001684489,0.0004815783,0.0005442919,0.0005845886,0.0004188454,0.001165511,0.0009587795,0.0009381585,0.001059276],"category_scores_gemma":[0.008803762,0.0004178388,0.0006058427,0.0004976663,0.001295381,0.002553487,0.001485471,0.001228216,0.0004125646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005112625,"about_ca_system_score_gemma":0.0005779215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007132918,"about_ca_topic_score_gemma":0.0008270903,"domain_scores_codex":[0.9988512,0.0004225883,0.00005452361,0.0001285369,0.0004910332,0.00005208532],"domain_scores_gemma":[0.9971379,0.001614652,0.0003472227,0.0004589802,0.0003759684,0.00006525459],"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":[0.0001083285,0.00004720577,0.0006279414,0.0002096577,0.00005967175,0.0002620708,0.0003609804,0.6430231,0.01237828,0.2499818,0.001580118,0.09136082],"study_design_scores_gemma":[0.00001088777,0.00007013384,0.0001946116,0.00001515189,0.00001034238,0.0001098303,0.00002179109,0.871038,0.002112074,0.1242556,0.002142054,0.00001957609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005404974,0.0002072323,0.9932159,0.0001169979,0.00001897115,0.000008168656,0.00001462153,0.0001181619,0.0008950491],"genre_scores_gemma":[0.6548422,0.0006910139,0.3413883,0.0001222688,0.00007649662,0.0001007126,0.0001435859,0.00006678698,0.002568667],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001684489,"threshold_uncertainty_score":0.00890851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02185407518873838,"score_gpt":0.2101637975341007,"score_spread":0.1883097223453623,"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."}}