{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002580221,0.0008652317,0.0005347288,0.004265218,0.0004504229,0.002407431,0.001627681,0.001100063,0.04911845],"category_scores_gemma":[0.009995945,0.000398624,0.0007650469,0.003763892,0.0002435185,0.003990697,0.004014278,0.001325049,0.02982122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005007744,"about_ca_system_score_gemma":0.0009507721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003536547,"about_ca_topic_score_gemma":0.005012597,"domain_scores_codex":[0.9988685,0.000347959,0.0001251326,0.0002409233,0.0002961457,0.0001214292],"domain_scores_gemma":[0.9930336,0.001908118,0.001077674,0.001472237,0.001514468,0.0009938374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001207733,0.0001321952,0.02130468,0.002845591,0.0001451185,0.0003747455,0.001439879,0.0006901409,0.002947934,0.003914,0.7364833,0.2285147],"study_design_scores_gemma":[0.0001349819,0.0002430637,0.04905052,0.001067592,0.0001359042,0.0006913009,0.002067082,0.007735203,0.003757907,0.005877532,0.9291031,0.0001358543],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.04370108,0.004864527,0.03348491,0.007548351,0.00157403,0.002479941,0.6851512,0.07160046,0.1495956],"genre_scores_gemma":[0.2204223,0.004384625,0.1684475,0.003491677,0.001720389,0.004838886,0.535176,0.009229149,0.05228944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04911845,"threshold_uncertainty_score":0.1643175,"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."}}