Exploring the potential for plant translocations to adapt to a warming world
Bibliographic record
Abstract
Abstract Anthropogenic climate warming is undisputed and yet, there is much that is unknown regarding biological impacts of changing temperature and precipitation, and the management options presented as solutions to biodiversity losses—such as translocations of plants and seeds—are often controversial. This Special Focus presents five new studies and two recently published articles in Journal of Ecology and Ecological Solutions and Evidence that analyse the use of existing plant translocations, assess the potential to use translocations to offset past declines and future population losses, and articulate the need to involve a broader range of stakeholders in the use of translocations. Our recommendations include improving the monitoring of plant translocations; harnessing existing translocations to form a global monitoring array for climate change impacts on biological diversity and strengthening research–practice linkages improving data sharing and broadening the stakeholders involved in translocations. Synthesis . Further study of translocations and integration of information from translocation projects holds the opportunity to provide a wealth of new knowledge and improved ways to counter climate change impacts on biological diversity and ecosystems.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".