Evaluating the Barrier Assessment Technique Derived from FishXing Software and the Upstream Movement of Brook Trout through Road Culverts
Bibliographic record
Abstract
Abstract Anthropogenic barriers to fish passage, such as culverts and dams, are major factors impeding the persistence and recovery of aquatic species. Considerable work has focused on mitigating these impacts; however, activities associated with measuring and restoring connectivity of aquatic ecosystems often face challenges in determining the passability of barriers by aquatic species. Hydrological modeling software that incorporates biological aspects of a focal species is often used as a relatively inexpensive method for assessing barrier passability for restoration decisions. However, the biological relevance of these approaches remains to be rigorously tested. We assessed passage rates of PIT‐tagged Brook Trout Salvelinus fontinalis through four road culverts and adjacent reference sites (unaltered areas of the streams) on the island of Newfoundland to determine whether upstream passage through road culverts was more restrictive than unaltered reference areas of the stream. Next, we examined the usefulness of barrier passability predictions derived from FishXing software by comparing them with in situ movement data for this species. Brook Trout passage for three of the four reference sites had a significantly higher range of passable stream flows compared with that for culverts, indicating the presence of velocity barriers in culverts. However, FishXing predictions of suitable fish passage discharges were conservative, and tagged fish successfully navigated partial barriers that were at least 2–3 times the upper limits of stream flow predicted to allow successful passage. The results of our study show a clear need for an improved understanding of fish movement through these structures so that barrier assessment techniques can be refined. The implications of not doing so may lead to restoration actions that result in limited biological benefit.
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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 | 0.000 |
| 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".