Restoring connectivity: A comparative analysis of culvert barrier remediation techniques in British Columbia
Bibliographic record
Abstract
ABSTRACT Objective Barrier culverts are one of the leading anthropogenic causes of stream fragmentation globally and a frequent target of restoration efforts. In British Columbia (B.C.), an estimated 92,000 culverts act as barriers to fish passage. Since 2008, two techniques (retrofits and replacements) have been used to remediate fish passage at approximately 200 barrier culverts in B.C. The retrofit technique involves leaving the culvert in place and installing baffles and/or weirs, whereas replacements involve removing the culvert and replacing it with an open-bottom structure. Rarely has the efficacy of these techniques or their compliance with best practices been evaluated. Methods We performed a posttreatment audit of culvert barrier remediations in B.C. through an examination of the current physical attributes at 15 retrofit and 15 replacement sites and scored the sites based on the B.C. culvert fish-passage assessment procedure and on best practice guidelines for stream crossings in B.C. Results We found that 80% of retrofits and 13% of replacements were classified as barriers based on the B.C. culvert fish-passage assessment. The sites that were classified as passable were shorter and did not constrict the stream relative to those classified as barriers. The retrofits did not meet the standards for best practices for remediating barrier culverts. The replacements aligned more with best practices, but improvements are needed to ensure that the structure span exceeds bank-full width and that natural stream morphology is maintained. Conclusions Although less expensive, the failure rates of retrofit remediations indicate that they should only be used as a stop gap or emergency technique until a replacement can be performed. Furthermore, our finding of continued noncompliance with best practices in B.C. highlights an issue that is likely affecting culvert remediations and new installations. This study demonstrates the need for rigorous compliance and effectiveness monitoring at future remediations to ensure their immediate and long-term success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".