{"id":"W2012739358","doi":"10.1016/j.envpol.2005.11.041","title":"Saturation models of arsenic, cobalt, chromium and manganese bioaccumulation by Hyalella azteca","year":2006,"lang":"en","type":"article","venue":"Environmental Pollution","topic":"Environmental Toxicology and Ecotoxicology","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Environment and Climate Change Canada","funders":"","keywords":"Bioaccumulation; Hyalella azteca; Arsenic; Environmental chemistry; Manganese; Chromium; Cobalt; Saturation (graph theory); Environmental science; Chemistry; Biology; Ecology; Inorganic chemistry; Crustacean","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001559721,0.000331815,0.0005008755,0.0003357103,0.0003694156,0.0005741406,0.0009044528,0.0007070171,0.001970268],"category_scores_gemma":[0.0005751122,0.0002316515,0.0004322745,0.0003546789,0.0003623528,0.0006336917,0.0005637938,0.0005056529,0.0003674023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002003028,"about_ca_system_score_gemma":0.0006604387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02882513,"about_ca_topic_score_gemma":0.01276106,"domain_scores_codex":[0.9999239,0.00001147028,0.000005213074,0.00002438646,0.00001578777,0.00001922747],"domain_scores_gemma":[0.9997836,0.0001079336,0.00002761595,0.00001211471,0.00005008783,0.00001870787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003849935,0.0001182914,0.00315987,0.000265714,0.00006802477,0.0002323085,0.0002368843,0.8438206,0.1167001,0.02636439,0.0007728533,0.007875911],"study_design_scores_gemma":[0.00002891942,0.00008177882,0.001095847,0.000006221076,0.00001948258,0.00003001668,0.00009438988,0.9771231,0.01492352,0.005497685,0.001080151,0.00001891661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9764995,0.0006467375,0.01237094,0.0005792418,0.00001726354,0.00003386504,0.0005718318,0.0001085029,0.009172086],"genre_scores_gemma":[0.9923227,0.0003575236,0.00129591,0.00004972059,0.000003730752,0.00003102664,0.0002230357,0.00001435642,0.005702053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02882513,"threshold_uncertainty_score":0.05731469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00399917166478389,"score_gpt":0.1762525313465344,"score_spread":0.1722533596817505,"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."}}