{"id":"W2911065313","doi":"10.2196/11477","title":"Wet Markets and Food Safety: TripAdvisor for Improved Global Digital Surveillance","year":2019,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Food Safety and Hygiene","field":"Agricultural and Biological Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Food safety; Business; Environmental health; Computer science; Risk analysis (engineering); Medicine; Food science; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001009795,0.0002239171,0.0004514189,0.00001279705,0.0002975136,0.0002347042,0.0001866261,0.0001131684,0.00005185778],"category_scores_gemma":[0.0001863842,0.0001055126,0.00007684131,0.0003221046,0.00007742913,0.0002833034,0.00009810962,0.0001057764,0.000009012228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004935527,"about_ca_system_score_gemma":0.00009707832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003886457,"about_ca_topic_score_gemma":0.0008459909,"domain_scores_codex":[0.997979,0.0001431973,0.000397277,0.0005615165,0.0001490945,0.0007699261],"domain_scores_gemma":[0.9986249,0.0004219049,0.0001633723,0.0001079563,0.0001014669,0.0005804333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004898181,0.00009432893,0.2523842,0.0001445243,0.00002556299,3.40612e-7,0.00004755147,1.174159e-7,0.00009936652,0.00135446,0.001962498,0.7433972],"study_design_scores_gemma":[0.00073815,0.001253243,0.5260231,0.000004753771,1.29703e-7,0.000009284115,0.0001089747,0.0002203065,2.669482e-7,0.0001643036,0.4712889,0.0001885199],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9465555,0.001678826,0.00004314668,0.04630401,0.0002658287,0.0013738,0.001920108,0.0001231955,0.001735603],"genre_scores_gemma":[0.9964651,0.0005168163,0.00006524042,0.001948475,0.0001586407,0.00005812551,0.0003531387,0.000002408456,0.000432091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7432087,"threshold_uncertainty_score":0.430268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01843580271878285,"score_gpt":0.2456397411148273,"score_spread":0.2272039383960445,"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."}}