{"id":"W2744732861","doi":"10.1016/j.envint.2017.07.023","title":"Human health risk assessment on the consumption of fruits and vegetables containing residual pesticides: A cancer and non-cancer risk/benefit perspective","year":2017,"lang":"en","type":"article","venue":"Environment International","topic":"Pesticide Exposure and Toxicity","field":"Agricultural and Biological Sciences","cited_by":143,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Institut National de Santé Publique du Québec","funders":"Ministère de la Santé et des Services sociaux","keywords":"Environmental health; Medicine; Pesticide; Toxicology; Attributable risk; Cancer; Population; Risk assessment; Public health; Consumption (sociology); Reference dose; Percentile; Biology; Internal medicine; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004506957,0.001240268,0.001331815,0.001741295,0.0002852894,0.001499883,0.001004044,0.001571683,0.004888177],"category_scores_gemma":[0.00327638,0.0003251722,0.002492995,0.0008922116,0.0008737351,0.0006205255,0.001052389,0.001129925,0.0004141339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00122426,"about_ca_system_score_gemma":0.001600887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00548243,"about_ca_topic_score_gemma":0.006600303,"domain_scores_codex":[0.9976374,0.00144372,0.00008758468,0.0001574898,0.0005481801,0.0001257394],"domain_scores_gemma":[0.9979906,0.001302444,0.0002014067,0.0001122028,0.0003317216,0.00006161648],"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":[0.01635992,0.002258544,0.1860744,0.01138218,0.02040569,0.00426866,0.001295926,0.1449476,0.05994854,0.07029229,0.01347241,0.4692939],"study_design_scores_gemma":[0.001734599,0.03178565,0.3950329,0.00588823,0.03696861,0.007610616,0.005553749,0.09070576,0.07597502,0.1665359,0.1817417,0.0004672767],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6378032,0.1317454,0.07196929,0.02356496,0.0007479311,0.001113766,0.00955754,0.0003250946,0.1231729],"genre_scores_gemma":[0.9572387,0.01823421,0.01111131,0.002509005,0.0002692503,0.0001981981,0.001115443,0.00003095199,0.009292963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00548243,"threshold_uncertainty_score":0.02383536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03717065319000552,"score_gpt":0.3170128250736645,"score_spread":0.279842171883659,"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."}}