Predicting road culvert passability for migratory fishes
Bibliographic record
Abstract
Abstract Aim Our goal was to predict road culvert passability, as defined by culvert outlet drop and outlet water velocity, for three fish swimming groups using remotely collected environmental variables that have been shown to influence the passability of road culverts. Locatio Laurentian Great Lakes Basin, north‐eastern North America, on the Canada–USA border. Methods We generated four boosted regression tree models, one for road culvert outlet drop and one each for the three culvert outlet water velocities, and predicted the probability of impassable road culverts on low‐order streams (Strahler 1‐4) based on the models. Independent variables in the models included the upstream area draining to the culvert, slope at the culvert, stream segment gradient and stream reach gradient. Results Gradient of the stream segment was the most important predictor in the outlet drop model, while upstream drainage area was the most important predictor in the three water velocity models. A majority of road culverts on low‐order streams are estimated to be passable even for weaker swimming fishes. Moderate to highly impassable road culverts are distributed across many low‐order streams throughout the basin, but particular regions are predicted to have higher densities than others due to topography. Main conclusions Predicted passability of road culverts by migratory fish is related to natural gradients in topography and stream size. While the probability of any particular culvert being impassable is low, the vast number of culverts in the basin means that, together, they could pose a greater challenge to migratory fish than dams. Our modelling framework could be used in any region where culverts are prevalent in the riverscape. The resulting estimates of passability to fishes can guide surveys towards the most problematic hydrological regions and structures and contribute to broad‐scale prioritization of barrier removals to restore ecological connectivity.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".