Balancing Environmental and Economic Objectives for In-Stream Construction Projects: A Case Study on Sediment Management
Bibliographic record
Abstract
Balancing economic efficiency with ecological risk management is a key challenge of the civil construction industry, especially for activities within the wetted perimeter of rivers (in-stream construction). Suspended sediment (SS) releases may occur and be harmful to aquatic flora and fauna. A tradeoff exists when completing in-stream construction quickly (shorter duration of exposure, DoE; h) with a more intense release (higher SS concentration, SSC; mg · L−1) versus completing the same activity more slowly with a less intense release. Environmental regulations often prioritize lower SSC limits, sacrificing economic efficiency. However, there may be preferable alternatives that balance economic and environmental objectives. This paper presents a study conducted during the construction of a gravel cofferdam in the Bow River, Calgary, Canada that compared two different gravel placement methods: slow placement by an excavator bucket and a faster method using a bulldozer to push gravel in the river. An SS yield parameter was introduced to contrast earthworks productivity and sediment loading characteristics by normalizing SS dose [product of SSC and DoE, suspended sediment dose (SSD); mg · h · L−1] to the quantity of gravel moved. The bulldozer method yielded greater economic efficiency without increased ecological risk associated with SS exposure, as defined by the management objective to protect salmonid fishes. This case study demonstrates the value of considering SS management tradeoffs by defining an operating limit using both SSC and DoE (or simply SSD) to achieve improved economic and environmental outcomes. However, implementing such an approach may face feasibility challenges depending on regulatory jurisdictions and may require changes in management policy to accommodate short-duration, high-intensity SS releases.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".