Characterisation of Beaver Habitat Parameters That Promote the Use of Culverts as Dam Construction Sites: Can We Limit the Damage to Forest Roads?
Bibliographic record
Abstract
The use of forest roads as foundations for dam construction by beavers is a recurrent problem in the management of forest road networks. In order to limit the damage to forest roads, our goal was to calculate the probability of beaver dam installation on culverts, according to surrounding habitat parameters, which could allow for improvement in the spatial design of new roads that minimise conflicts with beavers. Comparisons of culverts with (n = 77) and without (n = 51) dams in northwestern Quebec showed that catchment surface, cumulate length of all local streams within a 2-km radius, and road embankment height had a negative effect on the probability of dam construction on culverts, while flow level and culvert diameter ratio had a positive effect. Nevertheless, predicted probabilities of dam construction on culverts generally exceeded 50%, even on sites that were less favourable to beavers. We suggest that it would be more reasonable to take their probable subsequent presence into account at the earliest steps of road conception. Installing mitigation measures such as pre-dams during road construction would probably reduce the occurrence of conflicts with beavers and thus reduce the maintenance costs of forest roads.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".