{"id":"W2765645279","doi":"10.1111/sjos.12270","title":"Fast Inference for Network Models of Infectious Disease Spread","year":2017,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs; Cummings Foundation","keywords":"Inference; Epidemic model; Infectious disease (medical specialty); Outbreak; Mathematics; Context (archaeology); Stochastic modelling; Population; Statistics; Computer science; Artificial intelligence; Disease; Geography; Demography; Biology; Medicine; Virology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0008472691,0.0001699697,0.0006479648,0.00004783791,0.0003566782,0.00005053458,0.0004104188,0.00005628234,0.00002389427],"category_scores_gemma":[0.01477476,0.0001284435,0.0001541762,0.00004410591,0.0002862632,0.0001628879,0.0001283969,0.0001885403,0.000001212167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008234566,"about_ca_system_score_gemma":0.0001044166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002831529,"about_ca_topic_score_gemma":0.00003935182,"domain_scores_codex":[0.998465,0.00007544518,0.0007779202,0.0001348199,0.0002481741,0.0002986242],"domain_scores_gemma":[0.9936452,0.003465601,0.001753764,0.0003522894,0.0005598752,0.000223307],"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":[0.0006407673,0.0002905743,0.3218112,0.0009389099,0.0003872528,0.00009874916,0.0005300562,0.01232559,0.00001498739,0.6086283,0.03367861,0.02065509],"study_design_scores_gemma":[0.0007388275,0.0004009654,0.04961495,0.0004175022,0.0002076699,0.000005740159,0.00002834864,0.004802502,0.000006059425,0.9434139,0.0002250062,0.0001385606],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02661716,0.0002254994,0.9712738,0.0003289567,0.0004089236,0.0002454335,0.000508548,0.00001086115,0.0003808111],"genre_scores_gemma":[0.909033,0.000252742,0.09024493,0.00006044658,0.0002841288,0.000007410772,0.000002493782,0.00001653229,0.0000982994],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8824159,"threshold_uncertainty_score":0.9935242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2067492153497096,"score_gpt":0.4314864157348358,"score_spread":0.2247372003851262,"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."}}