{"id":"W1992589081","doi":"10.1016/j.scitotenv.2013.01.047","title":"Bioaccessibility and speciation of arsenic in country foods from contaminated sites in Canada","year":2013,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"Stantec (Canada); Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Genetic algorithm; Arsenic; Contamination; Environmental chemistry; Environmental science; Contaminated food; Chemistry; Biology; Ecology; Microbiology","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":[0.0004186866,0.00006332736,0.00009176019,0.00001952312,0.00005248126,0.000007587787,0.0002682903,0.00001668337,0.0006185336],"category_scores_gemma":[0.00005127016,0.00003973653,0.00001231803,0.0002339979,0.0007341802,0.0002129162,0.0002595636,0.0000647155,0.000006342415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006410466,"about_ca_system_score_gemma":0.00006438058,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6609609,"about_ca_topic_score_gemma":0.3059974,"domain_scores_codex":[0.9989883,0.00006005768,0.000239062,0.0001790745,0.0004046764,0.0001288446],"domain_scores_gemma":[0.9995342,0.00006327286,0.0001274965,0.0002432292,0.000003082115,0.00002874111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001307765,0.0001140827,0.3639742,0.00000626923,0.000004472762,4.278597e-7,0.002110804,0.01733842,0.6022854,0.0001487753,0.00003588598,0.01396813],"study_design_scores_gemma":[0.0001749779,0.00001077581,0.9534453,0.000009114893,0.000002854031,3.762309e-7,0.000692404,0.01783214,0.02731694,0.0004669112,0.000003698668,0.00004452897],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986154,0.00002308361,0.000004355153,0.0004117337,0.00004135517,0.0002912913,0.000007668839,0.00000117135,0.0006039419],"genre_scores_gemma":[0.9998037,0.00001279781,0.00005136567,0.00002154708,0.000002540557,0.000007524809,0.000001222645,0.000001913622,0.00009741677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.589471,"threshold_uncertainty_score":0.7066664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005243568177855243,"score_gpt":0.1785880411354482,"score_spread":0.173344472957593,"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."}}