{"id":"W3082605128","doi":"10.1016/j.sste.2022.100497","title":"Computationally efficient parameter estimation for spatial individual-level models of infectious disease transmission","year":2022,"lang":"en","type":"article","venue":"Spatial and Spatio-temporal Epidemiology","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Approximate Bayesian computation; Computation; Markov chain Monte Carlo; Computer science; Bayesian probability; Aggregate (composite); Statistics; Set (abstract data type); CAD; Data set; Population; Data mining; Algorithm; Artificial intelligence; Mathematics; Machine learning; Inference; Medicine; Engineering","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":[0.001889849,0.0002174691,0.0006640145,0.000138234,0.0002967695,0.00001026876,0.0001396226,0.00009087555,0.0001627881],"category_scores_gemma":[0.003237928,0.0001909308,0.0001377395,0.0001145547,0.000173206,0.00005208618,0.0001037845,0.0001977036,6.761911e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004252398,"about_ca_system_score_gemma":0.0001435333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000990179,"about_ca_topic_score_gemma":0.00006463758,"domain_scores_codex":[0.9971433,0.0009327331,0.000950174,0.000415543,0.0002635329,0.0002946665],"domain_scores_gemma":[0.991814,0.007173838,0.0005183701,0.0001715658,0.0001364359,0.0001858022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008944167,0.0004971957,0.01459723,0.0004579209,0.00008870794,0.000004699686,0.0007487073,0.07065873,0.0000231373,0.5711014,0.0004986799,0.3404292],"study_design_scores_gemma":[0.0004949948,0.0002977346,0.009804078,0.00001543374,0.00005363017,0.000002836208,0.00000938329,0.4885651,0.000009743403,0.5005835,0.00004901807,0.0001145552],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06355583,0.00006529505,0.9338654,0.0006345345,0.0001808363,0.0007460301,0.0008775512,0.00004144861,0.00003304716],"genre_scores_gemma":[0.5811374,0.000003180354,0.4181387,0.0001564071,0.00003706452,0.0001667747,0.0003388,0.00001369048,0.000007891879],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5175816,"threshold_uncertainty_score":0.7785932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1415889730728237,"score_gpt":0.3784635775609124,"score_spread":0.2368746044880887,"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."}}