{"id":"W3127790658","doi":"10.3390/toxics9020027","title":"Assessment of In Vitro Bioaccessibility and In Vivo Oral Bioavailability as Complementary Tools to Better Understand the Effect of Cooking on Methylmercury, Arsenic, and Selenium in Tuna","year":2021,"lang":"en","type":"article","venue":"Toxics","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Université de Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Bioavailability; Methylmercury; Chemistry; In vivo; Selenium; Food science; Arsenic; Tuna; Ingestion; Meal; Ex vivo; Gadus; Bioaccumulation; Environmental chemistry; In vitro; Biochemistry; Pharmacology; Fish <Actinopterygii>; Biotechnology; Biology; Fishery","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":[],"consensus_categories":[],"category_scores_codex":[0.001212427,0.0001123269,0.0002931684,0.0000473393,0.00003178368,0.00001538886,0.00007398493,0.00003373548,0.0002079325],"category_scores_gemma":[0.0001084359,0.00008222324,0.00002228985,0.0003144222,0.0002296461,0.0001114313,0.000219232,0.0001214629,8.443404e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001628673,"about_ca_system_score_gemma":0.00001600408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009119222,"about_ca_topic_score_gemma":0.003935363,"domain_scores_codex":[0.9986857,0.0003528855,0.0003332747,0.0002522806,0.0002117796,0.0001641198],"domain_scores_gemma":[0.9991965,0.0005066265,0.00005947545,0.0001895877,0.000005774135,0.00004203731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000874116,0.00006339434,0.8077514,0.00003947258,0.000007824842,0.000002346151,0.0008271536,0.00005371535,0.1870917,0.00001172306,0.0000309379,0.004032953],"study_design_scores_gemma":[0.000592853,0.0001287632,0.7071199,0.00002725333,0.000007500011,8.022993e-7,0.0009166073,0.0001430374,0.2907906,0.0001669094,0.00003734739,0.00006842452],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981792,0.00005312351,0.00002228752,0.000562874,0.00002549931,0.0003817922,0.00004249856,0.000002636338,0.0007300817],"genre_scores_gemma":[0.9992842,0.00002847352,0.0003713189,0.0002900492,0.000002926537,0.000009124769,0.000003135653,0.000003935831,0.000006809282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1036989,"threshold_uncertainty_score":0.3352966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03480310603250503,"score_gpt":0.3372435441488213,"score_spread":0.3024404381163163,"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."}}