{"id":"W1577594303","doi":"10.1007/978-3-7908-1767-6_5","title":"Line-Crawling Robot Navigation: A Rough Neurocomputing Approach","year":2003,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Crawling; Artificial intelligence; Computer science; Robot; Computer vision; Biology; Anatomy","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008538031,0.0007526578,0.001170087,0.0002512306,0.0007016391,0.000303262,0.0008167883,0.0002977206,0.000001215565],"category_scores_gemma":[0.0000947585,0.0006846394,0.0001758702,0.0002754949,0.0002691606,0.000234269,0.001692759,0.0009405675,0.000006418802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001037647,"about_ca_system_score_gemma":0.00005766593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007352489,"about_ca_topic_score_gemma":0.000001988613,"domain_scores_codex":[0.9963629,0.00008327673,0.0009971488,0.001407205,0.0004614918,0.0006879234],"domain_scores_gemma":[0.997978,0.000494954,0.0005183296,0.0006390679,0.0002578075,0.0001117985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001922068,0.0001733477,0.0006495662,0.002284541,0.0005228051,0.0008174295,0.01252788,0.06260438,0.000008630448,0.2755434,0.0008206044,0.6440282],"study_design_scores_gemma":[0.002139474,0.0003267817,0.0002946606,0.004625239,0.0001264983,0.0007125196,0.0007734407,0.8590174,0.000007852852,0.09403866,0.03436561,0.003571838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009769308,0.05272564,0.6953669,0.0008644228,0.004289537,0.001182869,0.000006031135,0.0006845541,0.2439031],"genre_scores_gemma":[0.4549268,0.008129549,0.5127673,0.003962193,0.004671105,0.00006375497,0.00008417543,0.0004139637,0.01498123],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7964131,"threshold_uncertainty_score":0.9995605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08161040223028829,"score_gpt":0.3008303432334915,"score_spread":0.2192199410032032,"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."}}