Review of science-based assessments of species vulnerability: Contributions to decision-making for assisted migration
Bibliographic record
Abstract
Assessing climate change impacts on forest species can significantly assist forest management planning. Recently, many tools have been developed for assessing species-specific vulnerability to climate change. These tools are question-based assessments that consider multiple criteria for individual species; the criteria are related to exposure and sensitivity to climate change. The following tools are discussed in relation to their use in Canada: (1) the NatureServe Climate Change Vulnerability Index; (2) the System for Assessing Vulnerability of Species to Climate Change (SAVS); (3) the Forest Tree Genetic Risk Assessment; (4) the Index for Predicting Tree Species Vulnerability; (5) ecological standards developed for the assisted migration of Torreya taxifolia; and (6) the Seeds of Success Program. These tools can all be applied to different forest species and they vary in such areas as their species-specific evaluation criteria, means for addressing uncertainty, and the integration of climate change models.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".