Identifying Western North American Tree Populations Vulnerable to Drought under Observed and Projected Climate Change
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
Global climate change has affected forest health and productivity. A highly visible, direct climate impact is dieback caused by drought periods in moisture-limited forest ecosystems. Here, we have used a climate moisture index (CMI), which has been developed in order to map forest–grassland transitions, to investigate the shifts of the zero-CMI isopleths, in order to infer drought vulnerabilities. Our main objective was to identify populations of the 24 most common western North American forest tree species that are most exposed to drought conditions by using a western North American forest inventory database with 55,700 plot locations. We have found that climate change projections primarily increase the water deficits for tree populations that are already in vulnerable positions. In order to test the realism of this vulnerability assessment, we have compared the observed population dieback with changes in index values between the 1961–1990 reference period and a recent 1991–2020 average. The drought impacts that were predicted by negative CMI values largely conformed to the observed dieback in Pinus edulis, Populus tremuloides, and Pinus ponderosa. However, there was one notable counter-example. The observed dieback in the Canadian populations of Populus tremuloides were not associated with directional trends in the drought index values but were instead caused by a rare extreme drought event that was not apparently linked to directional climate change. Nevertheless, a macro-climatic drought index approach appeared to be generally suitable to identify and forecast the drought threats to the tree populations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it