{"id":"W2185241920","doi":"10.6339/jds.201101_09(1).0005","title":"Estimating Transmissibility of Seasonal Influenza Virus by Surveillance Data","year":2021,"lang":"en","type":"article","venue":"Journal of Data Science","topic":"Influenza Virus Research Studies","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada","funders":"Public Health Agency; Public Health Agency of Canada","keywords":"Transmissibility (structural dynamics); Outbreak; Estimation; Transmission (telecommunications); Flu season; Seasonal influenza; Virus; Vaccination; Influenza A virus; Virology; Statistics; Biology; Computer science; Medicine; Mathematics; Coronavirus disease 2019 (COVID-19); Disease; Engineering; Infectious disease (medical specialty)","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":[0.006580694,0.0001098485,0.0004226906,0.00009846438,0.000165928,0.00006264912,0.002205427,0.00003139592,0.00009393124],"category_scores_gemma":[0.01202163,0.00008261368,0.00004139932,0.0008931615,0.001202214,0.002294322,0.00135834,0.0003927136,0.000003969507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007930442,"about_ca_system_score_gemma":0.002594978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003997041,"about_ca_topic_score_gemma":0.00001624939,"domain_scores_codex":[0.996529,0.0001016975,0.0007106377,0.00044801,0.001868695,0.0003419226],"domain_scores_gemma":[0.9956803,0.0003487134,0.0003964177,0.001934367,0.001360733,0.0002794827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005415491,0.0007070996,0.219478,0.0005746724,0.0002040258,0.0002942247,0.000459185,0.000105558,0.685939,0.00003674521,0.02312602,0.06853398],"study_design_scores_gemma":[0.008539447,0.001199046,0.6130591,0.002059504,0.000304762,0.00193875,0.0009568197,0.1382414,0.1443495,0.0003511925,0.08822603,0.0007744214],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9766737,0.00922522,0.008379192,0.001513591,0.0002713839,0.0001710576,0.003205441,0.00001222192,0.0005482211],"genre_scores_gemma":[0.8546209,0.00054552,0.1437077,0.0007272205,0.0002546216,6.068832e-7,0.00009236294,0.00001396245,0.00003709118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5415894,"threshold_uncertainty_score":0.9963005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2321728817170107,"score_gpt":0.4759016103250826,"score_spread":0.2437287286080719,"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."}}