Impacts of Highway Construction on Redd Counts of Stream-Dwelling Brook Trout
Bibliographic record
Abstract
Abstract Sedimentation during road construction is a human impact that threatens aquatic ecosystems. Despite a large body of literature on the effect of fine sediments on the initial developmental stages of fish, we do not know of any studies that have investigated the return of spawners to spawning grounds in streams impacted by sediment from road construction. The objective of this study was to quantify the return to spawning grounds of brook trout Salvelinus fontinalis at different stages of highway construction (before, during, and after construction). Redd counts were made at a fine spatial resolution (<0.5 m) over two consecutive years in 12 reaches distributed along a 115-km stretch of highway in the Laurentides Wildlife Reserve, Quebec. We found a significant decrease in redd counts in reaches affected by construction during the second year but no evidence of impacts in reaches affected by construction during the first year. A possible explanation is that sediment releases were well controlled during construction except after an extreme weather event occurring during the spawning season of the second year. However, we observed that a reach heavily impacted by sediments still supported high densities of spawners. Overall, we found a significant decrease in the absolute number of redd counts in the second year but strong consistency in the spatial distribution of the spawning sites, both within and among reaches and for all stages of highway construction and sediment loadings, which suggests that the return of spawners is more constrained by habitat variables than by sediment from highway construction. Received March 30, 2012; accepted August 20, 2012
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".