Maximizing Return on Investment for Island Restoration and Species Conservation
Bibliographic record
Abstract
Abstract Conservation practitioners are increasingly embracing evidence‐based and return on investment (ROI) approaches. Much evidence now exists that documents island biodiversity impacts by invasive mammals. The technical ability to eradicate invasive mammals from islands has increased exponentially; consequently, strategic planning focused on maximizing the ROI is now a limiting factor for island restoration. We use a regional ROI approach to prioritize eradications on islands for seabird conservation in British Columbia, Canada. We do so by integrating economic costs of interventions and applying a resource allocation approach. We estimate the optimal set of islands for eradication under two conservation objectives each with a series of increasing thresholds of population sizes and breeding locations. Our approach (1) identified the most cost‐effective interventions, (2) determined whether or not those interventions were nested with increasing thresholds, and (3) helped justify larger investments when appropriate. More often than not, conservation decisions are made at a regional scale, and decision‐makers often must make choices on how to allocate funds across a number of potential conservation actions. A regional, ROI framework can serve as a decision‐support tool for organizations engaging in discrete interventions in order to maximize benefits for the minimum cost.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".