Benthic injury dose–response models for polychlorinated biphenyl–contaminated sediment using equilibrium partitioning
Bibliographic record
Abstract
Abstract The study goal was to develop a sediment polychlorinated biphenyl (PCB) dose–response model based on benthic invertebrate effects to PCBs. The authors used an equilibrium partitioning (EqP) approach to generate predicted PCB sediment effect concentrations (largely Aroclor 1254) associated with a gradient of toxic effects in benthic organisms from effects observed in aquatic toxicity studies. The present study differs from all other EqP collective sediment investigations in that the authors examined a common dose–response gradient of effects for PCBs rather than a single, protective value. The authors reviewed the chronic aquatic toxicity literature to identify measured aqueous PCB concentrations and associated benthic invertebrate effects. The authors control-normalized the aquatic toxic effect data and expressed results from various studies as a common metric, percent injury. Then, they calculated organic carbon–normalized sediment PCB concentrations (mg/kg organic carbon) from the aqueous PCB toxicity data set using EqP theory based on the US Environmental Protection Agency's (EPIWEB 4.1) derivation of the water–organic carbon partition coefficient (KOC). Lastly, the authors constructed a nonlinear dose–response numerical model for these synoptic sediment PCB concentrations and biological effects: Y = 100/1 + 10([logEC50–logX] × [Hill slope]) (EC50 = median effective concentration). These models were used to generate “look-up” tables reporting percent injury in benthic biota for a range of Aroclor-specific sediment concentrations. For example, the model using the EPIWEB KOC estimate predicts mean benthic injury of 23.3%, 46.0%, 70.6%, 87.1%, and 95% for hypothetical sediment concentrations of 1 mg/kg, 2 mg/kg, 4 mg/kg, 8 mg/kg, and 16 mg/kg dry weight of Aroclor 1254, respectively (at 1% organic carbon). The authors recommend the model presented for screening but suggest, when possible, determining a site-specific KOC that, along with the tables and equations, allows users to create their own protective dose–response sediment concentration. Environ Toxicol Chem 2017;36:1311–1329. © 2016 SETAC
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".