{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001905083,0.000251557,0.0002297094,0.0003712855,0.0001665781,0.0001023679,0.0001567936,0.0001813331,0.00002716869],"category_scores_gemma":[0.00001440353,0.0002437837,0.00004320269,0.000325574,0.0001009562,0.0002697576,0.000009228961,0.0001314022,0.00001011681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002242342,"about_ca_system_score_gemma":0.00006221666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001241217,"about_ca_topic_score_gemma":0.00004756,"domain_scores_codex":[0.9984937,0.000003401007,0.0003611387,0.0003102979,0.0004131371,0.0004183447],"domain_scores_gemma":[0.999162,0.000008869885,0.0001013387,0.00009510779,0.0005816802,0.00005099823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002271219,0.0003807213,0.05409078,0.001134656,0.0004879094,0.000008967162,0.0008039901,0.2392504,0.6268884,0.02746948,0.01569597,0.03356159],"study_design_scores_gemma":[0.01042167,0.0007931732,0.005141434,0.001029574,0.0003430142,0.00009383418,0.003632616,0.3359522,0.6147078,0.007653052,0.01804375,0.002187839],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7944129,0.0001678888,0.2014511,0.000387509,0.0003250957,0.001370116,0.00001231812,0.0007960853,0.001077055],"genre_scores_gemma":[0.9882589,0.00005701318,0.01086862,0.00002545175,0.0001072499,0.000521861,0.00002353203,0.00004222135,0.00009515165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1938461,"threshold_uncertainty_score":0.9941211,"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."}}