{"id":"W2140478944","doi":"10.1140/epjb/e2006-00136-7","title":"Effects of population mixing on the spread of SIR epidemics","year":2006,"lang":"en","type":"article","venue":"The European Physical Journal B","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; Brock University","funders":"","keywords":"Mixing (physics); Homogeneous; Neighbourhood (mathematics); Geography; Population; Range (aeronautics); Epidemic model; Census; Mixing patterns; Distribution (mathematics); Statistical physics; Demography; Mathematics; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006787697,0.0006894022,0.001203436,0.001660828,0.001020089,0.002506115,0.000945157,0.001520672,0.006366934],"category_scores_gemma":[0.05627796,0.0005626962,0.0009640987,0.0007369284,0.001859397,0.00289153,0.00257038,0.001695047,0.0005086438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006100947,"about_ca_system_score_gemma":0.0003730385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001983684,"about_ca_topic_score_gemma":0.00119523,"domain_scores_codex":[0.9970521,0.00180836,0.0001361196,0.0003383547,0.0001894367,0.0004756872],"domain_scores_gemma":[0.9517272,0.03750196,0.005044,0.003108599,0.001009933,0.001608345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.009538638,0.001445165,0.1883199,0.0005610597,0.001666937,0.002863238,0.004572644,0.2769898,0.0488502,0.3383349,0.003696842,0.1231607],"study_design_scores_gemma":[0.0007330911,0.002247966,0.1055594,0.0001158512,0.00138722,0.001393422,0.001980253,0.7213497,0.007813046,0.1547805,0.002333377,0.0003061775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9787729,0.0004733412,0.01493445,0.0006076781,0.00004699491,0.00003668389,0.000105723,0.00009168117,0.004930628],"genre_scores_gemma":[0.9982241,0.0001792023,0.0007286955,0.00002900272,0.00003059277,0.000009812245,0.00002458524,0.00001243841,0.0007616397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006787697,"threshold_uncertainty_score":0.03589725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1054162020174181,"score_gpt":0.3612508239093506,"score_spread":0.2558346218919325,"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."}}