Ecological Risk Assessment (ERA) of Open-water Disposal of Sediment to Support the Management of Dredging Project in the St. Lawrence River
Bibliographic record
Abstract
The St. Lawrence River is subject to various anthropological pressures that can entail negative consequences for the ecosystem. As a result of the third and fourth St. Lawrence Action Plans, the current vision of sustainable management of this river and its main functions emphasizes the need for sound risk-based assessment approaches to support management decisions. More specifically, the sustainable navigation strategy, drawn up under St. Lawrence Action Plan III, explicitly identifies the need to develop sediment quality assessment tools, including those derived from ecotoxicological studies. The first management option addressed in this perspective was the open-water disposal of dredged sediments. In this context, an ecotoxico-logical risk assessment (ERA) approach using chemical characterization in Tier 1 and benthic organisms' toxicity tests in Tier 2 was elaborated based on physicochemical, toxicity testing, and benthic community structure data acquired from sediment samples collected in 59 sites along the St. Lawrence River. Hence this ERA approach will be used to determine whether the risk posed by the exposure of benthic organisms to dredged sediments at deposit sites and downstream is acceptable and compatible with open-water disposal.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".