{"id":"W2108556976","doi":"10.1038/sj.jea.7500433","title":"Reconstruction of methylmercury intakes in indigenous populations from biomarker data","year":2005,"lang":"en","type":"article","venue":"Journal of Exposure Science & Environmental Epidemiology","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Canada; Université de Montréal; Sante Montreal","funders":"","keywords":"Methylmercury; Mercury (programming language); Bioaccumulation; Chemistry; Environmental chemistry; Toxicokinetics; Population; Animal science; Excretion; Physiology; Biology; Environmental health; Medicine; Biochemistry; Metabolism","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003938321,0.0001483924,0.0004760271,0.0002095695,0.000152744,0.000007015733,0.0007675953,0.00009139268,0.001142901],"category_scores_gemma":[0.0009535997,0.0001204412,0.00007597708,0.0003381577,0.001718458,0.001353675,0.0003676489,0.0002316444,0.00004887954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00036627,"about_ca_system_score_gemma":0.0000425394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000363237,"about_ca_topic_score_gemma":0.0002054806,"domain_scores_codex":[0.9973776,0.000320862,0.001259207,0.000339437,0.000359964,0.0003428922],"domain_scores_gemma":[0.9980811,0.0003414308,0.0009827317,0.0004296096,0.00000773093,0.0001573624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002358015,0.00009545931,0.79526,8.697344e-7,0.00001142121,0.000002150386,0.000865175,0.00117994,0.09355685,0.00001554701,0.0002183416,0.1087706],"study_design_scores_gemma":[0.0003268745,0.0001358229,0.9928451,0.00001926049,0.0000244706,0.0001118021,0.0007669249,0.000283732,0.002447755,0.001473001,0.001446099,0.0001191943],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968798,0.001053146,0.0005584629,0.0006030104,0.0003945004,0.0001098312,0.00005322227,0.000003912333,0.0003441184],"genre_scores_gemma":[0.9788567,0.0004131017,0.02037557,0.0002388601,0.00007686633,0.000001337693,0.00001497834,0.000006726593,0.00001589484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.197585,"threshold_uncertainty_score":0.9997702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.105264942838307,"score_gpt":0.3556071752234447,"score_spread":0.2503422323851377,"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."}}