Prioritising culvert removals to restore habitat for at‐risk salmonids in the boreal forest
Bibliographic record
Abstract
Abstract In the boreal forests of Canada, industrial development has resulted in the installation of thousands of culverted road crossings that act as barriers to fish movement and degrade habitat for native freshwater fishes. In view of culvert removals being expensive, prioritisation methods have been developed, but the efficacy of such methods has not been thoroughly investigated nor have they been tested on low‐gradient boreal forest watersheds containing at‐risk salmonids. The management utility of a novel GIS‐based optimisation‐planning tool to prioritise fish barrier remediation was tested in two highly developed watersheds. Region‐specific parameter estimates of monetary variables (e.g. budget, individual barrier remediation costs), barrier passability and biologically relevant information for species on conservation concern (e.g. habitat suitability, dispersal ability) were incorporated. Results indicate that for Arctic grayling,Thymallus arcticusPallas, and bull trout,Salvelinus confluentusSuckley, a large proportion (~61–83%) of currently isolated habitat can be reconnected with low investment (~$200–$500 K). This study demonstrates the management utility of barrier optimisation methods for use in boreal watersheds, particularly as it significantly reduces the technical expertise needed to perform relatively complex optimisation analyses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".