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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".