A framework to guide the conservation of species hybrids based on ethical and ecological considerations
Bibliographic record
Abstract
Species hybrids have long been undervalued in conservation and are often perceived as a threat to pure species. Recently, the conservation value of hybrids, especially those of natural origin, has gained recognition; however, hybrid conservation remains controversial. We reviewed hybrid management policies, including laws, regulations, and management protocols, from a variety of organizations, primarily in Canada and the United States. We found that many policies are based on limited ethical and ecological considerations and provide little opportunity for hybrid conservation. In most policies, hybrids are either unrepresented or considered a threat to conservation goals. This is problematic because our review of the hybrid conservation literature identified many ethical and ecological considerations relevant to determining the conservation value of a hybrid, all of which are management-context specific. We also noted a lack of discussion of the ethical considerations regarding hybrid conservation. Based on these findings, we created a policy framework outlining situations in which hybrids could be eligible for conservation in Canada and the United States. The framework comprises a decision tree that helps users determine whether a hybrid should be eligible for conservation based on multiple ecological and ethical considerations. The framework may be applied to any hybrid and is flexible in that it accommodates context-specific management by allowing different options if a hybrid is a threat to or could benefit conservation goals. The framework can inform policy makers and conservationists in decision-making processes regarding hybrid conservation by providing a systematic set of decision criteria and guidance on additional criteria to be considered in cases of uncertainty, and it fills a policy gap that limits current hybrid management.
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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 | 0.057 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".