Extreme Climate Variability Should Be Considered in Forestry Assisted Migration: A Reply
Bibliographic record
Abstract
Responding to our recent article (Pedlar et al. 2012), Benito-Garzon and colleagues point out that extreme climatic events should be taken into account when selecting regenerative material for forestry-related assisted migration (AM) operations. Although technical considerations around seed movements were not the focus of our paper, we concur with their position and welcome the opportunity to expand on this topic. Benito-Garzon and colleagues emphasize the importance of considering extreme minimum temperatures when matching planting material and planting sites under climate change. Drought, heat waves, and spring freeze phenomena (Gu et al. 2008, Reyer et al. 2013) should also be recognized as extreme weather events that potentially play critical roles in determining the outcome of AM efforts. Although Benito-Garzon and colleagues raise the issue of climate extremes in the context of forestry AM, climate extremes are likely to play an important role in other types of AM, as well (e.g., species rescue; Pedlar et al. 2012).
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.045 | 0.055 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".