{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001719927,0.0004130727,0.0004955813,0.0006489701,0.0004180261,0.0009634303,0.0009340072,0.0008408846,0.001148045],"category_scores_gemma":[0.007883529,0.0003194073,0.0005975706,0.0004975065,0.00143071,0.002544454,0.001344175,0.001106497,0.0003078384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004990715,"about_ca_system_score_gemma":0.0006214254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006641586,"about_ca_topic_score_gemma":0.0008243718,"domain_scores_codex":[0.9990504,0.0003874808,0.00004673451,0.0001142181,0.0003567876,0.00004439753],"domain_scores_gemma":[0.9976196,0.001406637,0.000280014,0.0003521809,0.0002910078,0.00005054522],"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":[0.00008053508,0.00005022301,0.0006591012,0.0001817293,0.0000494849,0.000231675,0.0003505819,0.6214563,0.01080145,0.2781332,0.0008374096,0.08716843],"study_design_scores_gemma":[0.0000134725,0.00008749899,0.0002689269,0.00001523262,0.0000116094,0.00009702655,0.0000356055,0.867465,0.002286803,0.1276115,0.002088908,0.00001841343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00678519,0.0001404467,0.9920505,0.00009948527,0.00001287415,0.000009779369,0.00001293174,0.00008284227,0.0008060664],"genre_scores_gemma":[0.6122255,0.0004450555,0.3855019,0.00008391591,0.00004897345,0.00008498043,0.00008220247,0.00003509601,0.001492424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001719927,"threshold_uncertainty_score":0.009095907,"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."}}