{"id":"W2893646186","doi":"10.1016/j.foodres.2018.09.014","title":"An economic analysis of salmonella detection in fresh produce, poultry, and eggs using whole genome sequencing technology in Canada","year":2018,"lang":"en","type":"article","venue":"Food Research International","topic":"Salmonella and Campylobacter epidemiology","field":"Agricultural and Biological Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Ministry of Health, British Columbia; Genome Canada; McGill University; World Health Organization","keywords":"Salmonella; Outbreak; Economic cost; Cost estimate; Indirect costs; Biology; Biotechnology; Business; Economics; Virology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007837325,0.0000677413,0.0001824835,0.0003278082,0.00005659086,0.00001660706,0.000277752,0.00008107052,0.0001177887],"category_scores_gemma":[0.0001264031,0.00003705654,0.00002239268,0.0008185062,0.0001356162,0.00008556246,0.00009180811,0.0001771158,0.000002003335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006844462,"about_ca_system_score_gemma":0.0001320859,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6303421,"about_ca_topic_score_gemma":0.9627016,"domain_scores_codex":[0.9987996,0.0001730693,0.0002730213,0.000325302,0.000161325,0.0002676714],"domain_scores_gemma":[0.9995604,0.0001364721,0.00006882525,0.00006021914,0.0001233151,0.00005081804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002502255,0.00001326469,0.4995245,0.000001131068,0.00003932882,0.00000254223,0.00003163272,0.0003572926,0.4974743,0.00006406258,0.000003064092,0.002463837],"study_design_scores_gemma":[0.0001067979,0.0003104873,0.9600775,0.000009391231,0.00000697872,0.000008205775,0.001237308,0.02463303,0.01254879,0.0007232123,0.0002494739,0.00008879049],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989647,0.00005348938,0.000004491093,0.0006074186,0.00008922746,0.00009783349,0.00007796944,0.000005770814,0.00009915717],"genre_scores_gemma":[0.9997259,0.00001328699,0.00004335302,0.00002201144,0.0001397788,0.000008277969,0.0000388544,8.527671e-7,0.000007636661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4849255,"threshold_uncertainty_score":0.3721194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07388443565719173,"score_gpt":0.3430711165136284,"score_spread":0.2691866808564367,"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."}}