{"id":"W2395192821","doi":"10.1007/978-3-662-48971-0_30","title":"When Patrolmen Become Corrupted: Monitoring a Graph Using Faulty Mobile Robots","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; Université du Québec en Outaouais","funders":"Agence Nationale de la Recherche","keywords":"Patrolling; Computer science; Eulerian path; Enhanced Data Rates for GSM Evolution; Mobile robot; Domain (mathematical analysis); Robot; Graph; Line segment; Point (geometry); Algorithm; Robotics; Line (geometry); Combinatorics; Artificial intelligence; Theoretical computer science; Mathematics; Geometry","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":[0.001569619,0.0006038347,0.0006257731,0.00115115,0.0004264178,0.0009834869,0.003661125,0.0003737572,0.00003525917],"category_scores_gemma":[0.0000821879,0.000567128,0.0001554027,0.0008207738,0.0004915753,0.001142928,0.001943328,0.001168488,0.00004584644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005832886,"about_ca_system_score_gemma":0.0008744312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007507289,"about_ca_topic_score_gemma":0.00002173253,"domain_scores_codex":[0.9950004,0.00009000287,0.0006394137,0.001646995,0.001673104,0.0009500769],"domain_scores_gemma":[0.9967722,0.0002705353,0.0003401543,0.001501187,0.0006783271,0.0004375944],"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":[0.000004023394,0.00003000973,0.0001833609,0.00002629527,0.00001558198,0.00005748693,0.00138533,0.7654031,0.0001140183,0.001516251,0.0000341029,0.2312305],"study_design_scores_gemma":[0.0004094489,0.0002291229,0.00002251658,0.0003624912,0.000009849231,0.00006101024,9.183867e-7,0.9315049,0.0002657625,0.06510577,0.001267085,0.0007611351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001346842,0.0007824934,0.9939736,0.0002165048,0.002508434,0.0006875326,0.00000678064,0.0002699329,0.001420048],"genre_scores_gemma":[0.0419241,0.00009105888,0.9561707,0.0003957897,0.0006500689,0.00002888256,0.000008558767,0.00006659218,0.0006642162],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2304693,"threshold_uncertainty_score":0.999678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05845675204202838,"score_gpt":0.305469587630638,"score_spread":0.2470128355886096,"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."}}