{"id":"W2078297473","doi":"10.1897/08-204.1","title":"Addressing arsenic bioaccessibility in ecological risk assessment: A novel approach to avoid overestimating risk","year":2008,"lang":"en","type":"article","venue":"Environmental Toxicology and Chemistry","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; Royal Military College of Canada","funders":"","keywords":"Arsenic; Risk assessment; Tailings; Environmental science; Peromyscus; Environmental chemistry; Environmental health; Ecology; Biology; Chemistry; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004692146,0.001443099,0.001303467,0.001881896,0.0006182822,0.002211052,0.001393595,0.0007886231,0.0007129079],"category_scores_gemma":[0.01048296,0.0006607333,0.0007711484,0.001138535,0.001179676,0.002404391,0.002697115,0.001302165,0.0002007856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009760053,"about_ca_system_score_gemma":0.001411957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001815825,"about_ca_topic_score_gemma":0.005459088,"domain_scores_codex":[0.9967741,0.001584055,0.0002595083,0.0004014038,0.0009285406,0.00005234098],"domain_scores_gemma":[0.9942192,0.002481241,0.001619246,0.0005601254,0.001023715,0.00009657552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008669811,0.000631194,0.1626739,0.002338912,0.002079809,0.0007917045,0.003145577,0.05666191,0.1307784,0.02211246,0.002026928,0.6158924],"study_design_scores_gemma":[0.0003269124,0.006588189,0.1735005,0.0007842564,0.002697699,0.01118264,0.003929874,0.4310648,0.2373722,0.09620169,0.03532818,0.001023102],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3047874,0.002615606,0.6830058,0.002047696,0.00009247765,0.000355409,0.0003685672,0.0007859412,0.00594118],"genre_scores_gemma":[0.5209049,0.001307953,0.4759481,0.0002099319,0.00007866875,0.0002113429,0.000114372,0.00006239452,0.001162327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004692146,"threshold_uncertainty_score":0.02481478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02800997712141561,"score_gpt":0.2673527579158521,"score_spread":0.2393427807944365,"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."}}