Development of a National Fish Passage Database for Canada (CanFishPass): Rationale, Approach, Utility, and Potential Applicability to Other Regions
Bibliographic record
Abstract
Barriers resulting from anthropogenic activities (e.g., hydropower development, irrigation, flood control, low flow augmentation) can prevent the upstream migration of fish, reducing the connectivity of river systems. As a result, great efforts have been devoted to the design and installation of engineered fishways to enable the movement of fishes across barriers. However, the literature is generally devoid of scientific papers dealing with fishway design and effectiveness, making it difficult for those developing such facilities to determine which fishway designs are most appropriate for a given system and target species. One approach for providing information to support future fishway development is through the creation of databases that contain detailed accounts of existing facilities. Described here is the development of an engineered fishway database in Canada (called CanFishPass) intended to serve as a repository for information that has previously been difficult to find. The database includes detailed geo-referenced information such as engineering details, hydraulic characteristics, and biological effectiveness of one general class of fish passage facility (i.e., engineered fishways), as well as photographs and design drawings where available. The database is searchable by species, fishway type, and ecozone, and includes a reference section comprised of both peer-reviewed and grey literature. It is anticipated that the database will serve as an important resource for future fishway development projects enabling quantitative analyses, while also serving as the first inventory of engineered fishways in Canada. Although our efforts to date have been focused on Canada, the expansion to a global inventory of fishways would enable opportunities to learn directly from facility operations in other regions.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.027 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.012 |
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".