Fine-scale variability in growthclimate relationships of Douglas-fir, North Cascade Range, Washington
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
Information about the sensitivity to climate of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) is valuable because it will allow forest managers to maximize growth, better understand how carbon sequestration may change over time, and better model and predict future ecosystem responses to climatic change. We examined the effects of climatic variability on the growth of Douglas-fir along an elevational gradient in the North Cascade Range, Washington (USA), at annual timescales during the 20th century. Multivariate analysis and correlation analysis were used to identify climate-growth relationships. Mid-elevation chronologies were negatively correlated with growing season maximum temperature and positively correlated with growing season precipitation. In contrast, high-elevation chronologies were positively correlated with annual temperatures and negatively correlated with previous-year winter Pacific Decadal Oscillation index. Projected increases in summer temperatures will likely cause greater soil moisture stress in many forested ecosystems. The potential of extended summer drought periods over decades may significantly alter spatial patterns of productivity, thus impacting carbon storage. It is likely that the productivity of Douglas-fir in the Cascade Range will decrease at sites with shallow, excessively drained soils, south- and west-facing aspects, and steep slopes and will increase at high-elevation sites.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it