Watershed-scale effectiveness of floodplain habitat restoration for juvenile coho salmon in the Chilliwack River, British Columbia
Bibliographic record
Abstract
Although billions of dollars have been spent restoring degraded watersheds worldwide, watershed-scale studies evaluating their effectiveness are rare. To mitigate damage from past logging activities, the floodplain of the upper Chilliwack River watershed (∼600 km 2 ) was extensively restored from 1996 to 2000 through off-channel habitat restoration. The contribution of restored habitat to watershed-scale production of wild coho (Oncorhynchus kisutch) smolts was estimated through an extensive mark–recapture program in 2002. 27%–34% of the production of the estimated 247 200 out-migrating coho smolts could be attributed to the 157 000 m 2 of newly created habitat. Area-based habitat models from the literature performed reasonably well in predicting smolt production from restored habitat, providing an acceptable first-order approach for evaluating production benefits of restoration. The costs of smolt production integrated over 30 years ranged from US$0.69–US$10.05 per smolt, falling within the range of hatchery production costs reported elsewhere (typical cost of ∼US$1.00 per smolt) at the most cost-effective restoration sites. This study demonstrates that large-scale habitat restoration can effectively enhance fish production at a watershed scale, at a cost that may be comparable to hatchery smolt production.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".