Predictive ability of sediment quality guidelines and design of a tier1 risk assessment framework for dredged sediments: how to deal with confounding factors in practice ?
Bibliographic record
Abstract
Many tiered frameworks designed for contaminated sediment risk assessment rely upon sediment quality guidelines (SQG) at the first tier. In case of multiple contaminations, results can be aggregated in indices such as mean quotients. It can thus be decided e.g. to dispose on dredged materials in open water without further investigation, provided SQGs, or specific values of indices derived from SQGs, are not exceeded. Thus, the relevance of SQGs, and indices as well, is critical for environment protection. In the context of the development of a tiered framework for dredged materials assessment for the St Lawrence River, we assessed various indices based on the SQGs available for this stream and a database matching chemistry and toxicity tests. As the overall efficiency of any of the tested indices remained rather low, factors such as sediment grain size, nutrients, metal-binding phases, which could explain type II errors (false negatives), were examined. This lead to the design of a modified tier I, where SQGs are used in combination with decision rules based on some explanatory factors. This work is supported by Environment Canada and the Ministère du Développement Durable, de l'Environnement et des Parcs du Québec.
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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 | 0.032 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".