{"id":"W2051632436","doi":"10.1007/s10479-015-1797-5","title":"New local searches for solving the multi-source Weber problem","year":2015,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Military College of Canada; Royal Ottawa Mental Health Centre","funders":"Natural Sciences and Engineering Research Council of Canada; National Research University Higher School of Economics; Russian Science Foundation","keywords":"Heuristics; Delaunay triangulation; Heuristic; Theory of computation; Metaheuristic; Triangulation; Mathematical optimization; Computer science; Constructive; Variety (cybernetics); Decomposition; Quality (philosophy); Mathematics; Algorithm; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003562653,0.00009194545,0.0001095872,0.0002614187,0.0004668156,0.00032976,0.0004470432,0.00003998482,0.0002412443],"category_scores_gemma":[0.0007713966,0.00006716292,0.00006655079,0.000702885,0.0001462739,0.0006890096,0.0002853832,0.0001604218,0.0003970205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001853632,"about_ca_system_score_gemma":0.0001783521,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01204703,"about_ca_topic_score_gemma":0.007421392,"domain_scores_codex":[0.9984866,0.00004039541,0.000278453,0.0002198954,0.0006040086,0.0003706506],"domain_scores_gemma":[0.9976887,0.00005621923,0.00001393308,0.0003411658,0.001860136,0.00003983674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008261809,0.0002730682,0.0005347494,0.0002494234,0.0000768616,5.304541e-7,0.001146616,0.2183728,0.000309759,0.1003907,0.627121,0.0514419],"study_design_scores_gemma":[0.000432175,0.00003202071,0.0004494313,0.00002556568,0.000007117783,2.043985e-7,0.003273589,0.4888556,0.0004999134,0.001042715,0.5052701,0.0001115797],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07769984,0.0009532618,0.659453,0.2282987,0.0004370279,0.005025736,0.00001594291,0.0001896427,0.02792685],"genre_scores_gemma":[0.9594324,0.00004105446,0.004961507,0.001274878,0.0005283488,0.00022997,0.00005484594,0.00002495914,0.03345202],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8817326,"threshold_uncertainty_score":0.9945318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.513336972344693,"score_gpt":0.4481032581850867,"score_spread":0.06523371415960633,"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."}}