Peer Review #1 of "Cost-benefit analysis for invasive species control: the case of greater Canada goose Branta canadensis in Flanders (northern Belgium) (v0.1)"
Bibliographic record
Abstract
Background.Sound decisions on control actions for established invasive alien species (IAS) require information on ecological as well as socio-economic impact of the species and of its management.Costbenefit analysis provides part of this information, yet has received relatively little attention in the scientific literature on IAS.Methods.We apply a bio-economic model in a cost-benefit analysis framework to greater Canada goose Branta canadensis, an IAS with documented social, economic and ecological impacts in Flanders (northern Belgium).We compared a business as usual (BAU) scenario which involved non-coordinated hunting and egg destruction with an enhanced scenario based on a continuation of these activities but supplemented with coordinated capture of moulting birds.To assess population growth under the BAU scenario we fitted a logistic growth model to the observed pre-moult capture population.Projected damage costs included water eutrophication and damage to cultivated grasslands and were calculated for all scenarios.Management costs of the moult captures were based on a representative average of the actual cost of planning and executing moult captures. Results.Comparing the scenario's with different capture rates, different costs for eutrophication and various discount rates, showed avoided damage costs were in the range of 21,15 M€ to 45,82 M€ under the moult capture scenario.The lowest value for the avoided costs applied to the scenario where we lowered the capture rate by 10%.The highest value occurred in the scenario where we lowered the real discount rate from 4% to 2.5%.Discussion.The reduction in damage costs always outweighed the additional management costs of moult captures.Therefore, additional coordinated moult captures could be applied to limit the negative economic impact of greater Canada goose at a regional scale.We further discuss the strengths and weaknesses of our approach and its potential application to other IAS.
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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 | 0.019 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.145 | 0.047 |
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".