{"id":"W1043292916","doi":"10.1007/978-3-319-18356-5_3","title":"Improvements to the Linear Optimization Models of Patrol Scheduling for Mobile Targets","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke; Université du Québec à Montréal","funders":"","keywords":"Computer science; Column generation; Stackelberg competition; Scalability; Robustness (evolution); Scheduling (production processes); Linear programming; Mathematical optimization; Software; Distributed computing; Algorithm; Programming language; Operating system","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":[0.0005759627,0.0002380737,0.0003230002,0.0002753699,0.00009559187,0.00004948719,0.0006756292,0.0001593126,0.00001144913],"category_scores_gemma":[0.00005519072,0.0001769764,0.0001007569,0.0002712108,0.0001343941,0.0001755014,0.0001308585,0.0002624416,0.000001937077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001522637,"about_ca_system_score_gemma":0.0001359274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008371393,"about_ca_topic_score_gemma":0.00001701522,"domain_scores_codex":[0.9985316,0.000008043206,0.0003535489,0.000401913,0.0004129539,0.000291959],"domain_scores_gemma":[0.9989424,0.0001075053,0.00008213795,0.0004764032,0.0003161645,0.00007533836],"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":[0.000004335681,0.000003442992,0.000002185662,0.0000503931,0.00001311264,2.674423e-7,0.000290908,0.9237815,0.0001073532,0.0000973955,0.000008457183,0.07564069],"study_design_scores_gemma":[0.00009919405,0.00009246098,6.87018e-7,0.00007327872,0.00001824464,7.13142e-7,7.184804e-7,0.9859651,0.001588216,0.01168134,0.0002975953,0.0001824989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001669801,0.0005439293,0.9979395,0.00004243732,0.0003821324,0.0006641789,0.00001927307,0.00003817144,0.0002034456],"genre_scores_gemma":[0.4170647,0.00003591024,0.5821747,0.000157115,0.0004193581,0.00005472523,0.00001591612,0.00003912156,0.00003847498],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4168977,"threshold_uncertainty_score":0.7216887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01514660464138608,"score_gpt":0.2477507644427814,"score_spread":0.2326041598013953,"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."}}