{"id":"W2566497556","doi":"10.22360/summersim.2016.scsc.060","title":"Population Modelling by Examples II","year":2016,"lang":"en","type":"article","venue":"","topic":"Mathematical and Theoretical Epidemiology and Ecology Models","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Windsor; University of Ottawa","funders":"","keywords":"Computer science; Field (mathematics); Population; Domain (mathematical analysis); Data science; Work (physics); Enhanced Data Rates for GSM Evolution; Management science; Artificial intelligence; Engineering; Sociology; Mathematics; Mechanical engineering","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":[0.001641658,0.0007628637,0.0005741973,0.00140638,0.001242165,0.002234595,0.001327512,0.001550279,0.01338862],"category_scores_gemma":[0.008271125,0.0002107522,0.001094032,0.001435143,0.001731728,0.003093286,0.002241326,0.003011141,0.002746132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001047524,"about_ca_system_score_gemma":0.0006241321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002543846,"about_ca_topic_score_gemma":0.001947791,"domain_scores_codex":[0.9980795,0.001158067,0.0001144485,0.0001789928,0.000360108,0.0001088419],"domain_scores_gemma":[0.9976146,0.001518639,0.0001647204,0.0003026027,0.0003076722,0.00009179137],"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":[0.000007562325,0.00001103725,0.0002703498,0.00008946158,0.000007333768,0.0001488072,0.000230153,0.004222333,0.00008290826,0.9640173,0.01770038,0.01321238],"study_design_scores_gemma":[0.000006339456,0.00001349036,0.0002072062,0.0001054622,0.000007576758,0.0004286011,0.0001487105,0.01422431,0.0001262113,0.8475131,0.1372069,0.00001212626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008368711,0.008645284,0.5941551,0.01794297,0.001870913,0.0003296823,0.0007954324,0.0004384287,0.3674535],"genre_scores_gemma":[0.3566626,0.01472546,0.5065005,0.004536274,0.00204625,0.001324837,0.001482648,0.0004415364,0.1122799],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01338862,"threshold_uncertainty_score":0.04478931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.049820751613357,"score_gpt":0.2906015332373111,"score_spread":0.2407807816239541,"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."}}