{"id":"W4415555246","doi":"10.1007/978-3-031-95115-2_16","title":"Comparing External and Internal Control Measures for COVID 19: Lessons from Comprehensive Mobility-Based Epidemic Simulation Models for the City of Montreal and Hong Kong","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Control (management); Coronavirus disease 2019 (COVID-19); Internal model; Pandemic; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Constant (computer programming)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006454503,0.0004537432,0.001289566,0.0001343299,0.000117861,0.00002587134,0.000207026,0.0003355809,0.000004162694],"category_scores_gemma":[0.009555243,0.0003464799,0.0002315915,0.0000320059,0.0001335326,0.0000425439,0.0001322838,0.0005089481,2.370305e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002163147,"about_ca_system_score_gemma":0.00005116278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009361054,"about_ca_topic_score_gemma":0.005161218,"domain_scores_codex":[0.9982281,0.00005312256,0.0007357104,0.0005242851,0.0001747271,0.0002840221],"domain_scores_gemma":[0.9372836,0.06191181,0.0003173087,0.0002723931,0.0001378733,0.00007698238],"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":[0.0002432895,0.00001364633,0.002154546,0.001081082,0.0002777873,0.000001232135,0.0001891184,0.9882456,0.000121325,0.004839363,0.00001126628,0.002821712],"study_design_scores_gemma":[0.001168339,0.00003533962,0.002024508,0.0007441106,0.0002286977,4.991699e-7,0.000002675578,0.7348496,0.00002635269,0.2605551,0.0001566858,0.000208101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00266374,0.006790775,0.9878336,0.000560439,0.0001140554,0.001563212,0.000348661,0.000068161,0.00005741076],"genre_scores_gemma":[0.9871401,0.0001118204,0.01206106,0.0003382245,0.0001324756,0.000139232,0.00001423769,0.00003895558,0.0000238882],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9844764,"threshold_uncertainty_score":0.9998987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1859186607982893,"score_gpt":0.3747003597005716,"score_spread":0.1887816989022823,"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."}}