Efficiency of sediment quality guidelines for predicting toxicity: The case of the St. Lawrence river
Bibliographic record
Abstract
Multitiered frameworks that are designed for risk assessment of contaminated sediment rely on sediment quality guidelines (SQGs) at the first tier or screening level. In the case of contamination by multiple pollutants, results can be aggregated under indices such as the mean quotient. A decision is then reached (e.g., to dispose of dredged materials in open water) without further investigation, provided that the SQGs or the specific values of indices or quotients derived from the SQGs are not exceeded. In this way, SQGs and quotients play a critical role in environmental protection. As part of the development of a tiered framework to assess the environmental risk of materials dredged from the St. Lawrence River, we evaluated various quotients based on SQGs available for this river with a data set that matches chemistry and toxicity test endpoints. The overall efficiency of all tested quotients was rather low, and we then examined factors such as sediment grain size, nutrients, metal-binding phases (e.g., Al, Fe), and dissolved organic carbon to explain misclassified samples. This examination led to the design of a modified tier 1 framework in which SQGs are used in combination with decision rules based on certain explanatory factors.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".