Mitigating infrastructure loss from beaver flooding: A cost–benefit analysis
Bibliographic record
Abstract
We installed 12 pond levelers to counter flooding by beavers and developed a cost–benefit analysis for these sites in Alberta, Canada. We also documented beaver management approaches throughout Alberta. Over 3 years, one site required regular maintenance until we designed a modified pond leveler; another required minor modifications. Others required almost no maintenance. Based on a “willingness-to-pay” (WTP) of $0 and discount rate of 3%, installing pond levelers resulted in a present value net benefit of $81,519 over 3 years and $179,440 over 7 years. Scenarios incorporating discount rates of 3% and 7%, horizons of either 3 or 7 years, and varying WTPs resulted in significant net benefits. Provincially, municipalities employed up to seven methods to control beavers: most commonly lethal control and dam removal. Total annual costs provided by 48 municipalities and 4 provincial parks districts were $3,139,223; however, cost-accounting was sometimes incomplete, which makes this a conservative estimate.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".