Uncovering antagonisms in recovery planning for species at risk: A diagnostic approach
Bibliographic record
Abstract
Abstract Amid Earth's ongoing sixth mass extinction event, numerous measures have been proposed to recover the populations of species at risk of extinction. However, the methods and objectives of different species' recovery plans sometimes conflict with each other, causing a conundrum we refer to as recovery – action antagonism . Recovery–action antagonism reduces the cost‐effectiveness of conservation programs and can increase the extinction risk of nontarget species. We describe a method to identify interactions between recovery actions, including antagonisms proposed for different at‐risk species in a given location. The method includes a process to evaluate potential drivers of recovery‐action antagonism and other interaction types using principal coordinates analysis and distance‐based redundancy analysis. We illustrate various applications of the method through case studies performed in Pelee Island and Rouge National Urban Park, two biodiverse areas in Ontario, Canada. Potential antagonism was identified between 1.5% (Pelee) and 5% (Rouge) of the evaluated recovery actions. Although the rate of antagonism was low in our case studies, the method allows the identification of a variety of interactions, which can help to prioritize similar and complementary actions that will benefit a large number of species while minimizing actions that may have competing outcomes.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".