{"id":"W2023201554","doi":"10.1016/j.envpol.2013.11.012","title":"Metal and proton toxicity to lake zooplankton: A chemical speciation based modelling approach","year":2013,"lang":"en","type":"article","venue":"Environmental Pollution","topic":"Environmental Toxicology and Ecotoxicology","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; Laurentian University","funders":"Naturvårdsverket; Grantová Agentura České Republiky; Sight Research UK; Environment Agency; Natural Environment Research Council; U.S. Environmental Protection Agency","keywords":"Genetic algorithm; Zooplankton; Environmental chemistry; Metal toxicity; Environmental science; Chemical toxicity; Metal; Oceanography; Chemistry; Ecology; Heavy metals; Biology; Geology; Water pollutants","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002107519,0.0002632602,0.0002184988,0.00005855275,0.000252907,0.00003455702,0.0001594115,0.0002279536,0.006970517],"category_scores_gemma":[0.000009619063,0.0002637053,0.00006462215,0.0001294868,0.0003537761,0.0003954436,0.0002636837,0.0002191392,0.001887783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004097899,"about_ca_system_score_gemma":0.000002958135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003242865,"about_ca_topic_score_gemma":0.00001574144,"domain_scores_codex":[0.9982362,0.0000938825,0.0002837842,0.0006346055,0.0002937205,0.0004578241],"domain_scores_gemma":[0.9993805,0.00002258765,0.00008848283,0.0002430615,6.367078e-7,0.0002647707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001115928,0.00106978,0.04408889,0.00001844816,0.00002900274,0.000004639859,0.0003111221,0.06644654,0.8805958,0.00004678665,0.001408829,0.005868584],"study_design_scores_gemma":[0.002458882,0.000887289,0.3153364,0.00001816661,0.0000929399,0.00009774479,0.0003318368,0.2365208,0.4275416,0.001301823,0.01390104,0.001511419],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9812137,0.00001725533,0.01190548,0.0004361831,0.00006580421,0.001509137,0.00003562982,0.00005195468,0.004764842],"genre_scores_gemma":[0.9878322,0.000009919841,0.009611202,0.001266978,0.00006883215,0.0004471943,0.00008015025,0.00002450309,0.0006590104],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4530542,"threshold_uncertainty_score":0.9999815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009137654816900568,"score_gpt":0.1838474208541624,"score_spread":0.1747097660372618,"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."}}