Advantages of model averaging of species sensitivity distributions used for regulating produced water discharges
Bibliographic record
Abstract
Produced water (PW) generated by Australian offshore oil and gas activities is typically discharged to the ocean after treatment. These complex mixtures of organic and inorganic compounds can pose significant environmental risk to receiving waters, if not managed appropriately. Oil and gas operators in Australia are required to demonstrate that environmental impacts of their activity are managed to levels that are as low as reasonably practicable, for example, through risk assessments comparing predicted no-effect concentrations (PNECs) with predicted environmental concentrations of PW. Probabilistic species sensitivity distribution (SSD) approaches are increasingly being used to derive PW PNECs and subsequently calculating dilutions of PW (termed "safe" dilutions) required to protect a nominated percentage of species in the receiving environment (e.g., 95% and 99% or PC95 and PC99, respectively). Limitations associated with SSDs include fitting a single model to small (six to eight species) data sets, resulting in large uncertainty (very wide 95% confidence limits) in the region associated with PC99 and PC95 results. Recent advances in SSD methodology, in the form of model averaging, claim to overcome some of these limitations by applying the average model fit of multiple models to a data set. We assessed the advantages and limitations of four different SSD software packages for determining PNECs for five PWs from a gas and condensate platform off the North West Shelf of Australia. Model averaging reduced occurrences of extreme uncertainty around PC95 and PC99 values compared with single model fitting and was less prone to the derivation of overly conservative PC99 and PC95 values that resulted from lack of fit to single models. Our results support the use of model averaging for improved robustness in derived PNEC and subsequent "safe" dilution values for PW discharge management and risk assessment. In addition, we present and discuss the toxicity of PW considering the paucity of such information in peer-reviewed literature. Integr Environ Assess Manag 2024;20:498-517. © 2023 Commonwealth Scientific and Industrial Research Organisation. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".