Extreme Climate Variability Should Be Considered in Forestry Assisted Migration: A Reply
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it