{"id":"W2121788643","doi":"10.1007/s11590-011-0412-1","title":"Incorporating the threat of terrorist attacks in the design of public service facility networks","year":2011,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Tabu search; Planner; Computer science; Heuristic; Operations research; Service (business); Maximization; Service level; Computational intelligence; Bilevel optimization; Facility location problem; Network planning and design; Level of service; Mathematical optimization; Transport engineering; Optimization problem; Business; Engineering; Computer network; Mathematics; Marketing; Artificial intelligence","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":[0.001319168,0.0001092113,0.0001216556,0.0001105263,0.0001054077,0.00005038397,0.0004248978,0.00003295873,0.0002420854],"category_scores_gemma":[0.00007162557,0.00007174905,0.00003848862,0.000927265,0.00008003601,0.0005531344,0.0000857218,0.00008568066,0.00001164501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001427822,"about_ca_system_score_gemma":0.000008519693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001639975,"about_ca_topic_score_gemma":0.0003573649,"domain_scores_codex":[0.99899,0.00009076147,0.0004085039,0.0001552606,0.0002108185,0.0001446823],"domain_scores_gemma":[0.9992537,0.00003426796,0.0002086474,0.0003468304,0.0001517227,0.000004828009],"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":[0.00001434528,0.00006778018,0.01718855,0.00007102229,0.00001349701,3.528047e-7,0.0005600862,0.9787564,0.00002042444,0.001480206,0.00116931,0.000658039],"study_design_scores_gemma":[0.0002409081,0.000005510066,0.01524031,0.00001608937,0.00002661496,1.559549e-7,0.001454331,0.9823998,0.000008860663,0.00007982797,0.0004164195,0.0001111949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03324005,0.00002056259,0.951379,0.01171321,0.0001931785,0.0006436466,0.000002200481,0.00003276587,0.002775397],"genre_scores_gemma":[0.9921589,0.000006672778,0.001931961,0.005758965,0.00004616597,0.00003432399,0.00004693423,0.000006031614,0.000009996589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9589189,"threshold_uncertainty_score":0.2925841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09519457258726062,"score_gpt":0.2239190677047913,"score_spread":0.1287244951175306,"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."}}