Assessment of hazard metrics for predicting field benthic invertebrate toxicity in the Detroit River, Ontario, Canada
Bibliographic record
Abstract
Abstract Numerical sediment quality guidelines (SQGs) are frequently used to interpret site-specific sediment chemistry and predict potential toxicity to benthic communities. These SQGs are useful for a screening line of evidence (LOE) that can be combined with other LOEs in a full weight of evidence (WOE) assessment of impacted sites. Three common multichemical hazard quotient methods (probable effect concentration [PEC]-Qavg, PEC-Qmet, and PEC-Qsum) and a novel (hazard score [HZD]) approach were used in conjunction with a consensus-based set of SQGs to evaluate the ability of different scoring metrics to predict the biological effects of sediment contamination under field conditions. Multivariate analyses were first used to categorize river sediments into distinct habitats based on a set of physicochemical parameters to include gravel, low and high flow sand, and silt. For high flow sand and gravel, no significant dose–response relationships between numerically dominant species and various toxicity metric scores were observed. Significant dose–response relationships were observed for chironomid abundances and toxicity scores in low flow sand and silt habitats. For silt habitats, the HZD scoring metric provided the best predictor of chironomid abundances compared to various PEC-Q methods according to goodness-of-fit tests. For low flow sand habitats, PEC-Qsum followed by HZD, provided the best predictors of chironomid abundance. Differences in apparent chironomid toxicity between the 2 habitats suggest habitat-specific differences in chemical bioavailability and indicator taxa sensitivity. Using an IBI method, the HZD, PEC-Qavg, and PEC-Qmet approaches provided reasonable correlations with calculated IBI values in both silt and low flow sand habitats but not for gravel or high flow sands. Computation differences between the various multi-chemical toxicity scoring metrics and how this contributes to bias in different estimates of chemical mixture toxicity scores are discussed and compared. Integr Environ Assess Manag 2017;13:410–422. © 2016 SETAC Key Points Field assessment of multichemical hazard metrics for predicting benthic invertebrate toxicity in the Detroit River, Ontario, Canada. Multivariate analyses were used to categorize river sediments into distinct habitats based on various physicochemical parameters. Significant dose-response relationships were observed for Chironomid abundances and toxicity scores in 2 habitats. Differences in Chironomid toxicity suggest habitat specific differences in chemical bioavailability and indicator taxa sensitivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".