Spatial variability in forest growth – climate relationships in the Olympic Mountains, Washington
Why this work is in the frame
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Bibliographic record
Abstract
For many Pacific Northwest forests, little is known about the spatial and temporal variability in tree growth – climate relationships, yet it is this information that is needed to predict how forests will respond to future climatic change. We studied the effects of climatic variability on forest growth at 74 plots in the western and northeastern Olympic Mountains. Basal area increment time series were developed for each plot, and Pearson's correlation analysis and factor analysis were used to quantify growth–climate relationships. Forest growth in the Olympic Mountains responds to climatic variability as a function of mean climate and elevation. Low summer moisture limits growth across all elevations in the dry northeastern Olympics. Growth at low elevations in the wet western Olympics is associated with phases of the Pacific Decadal Oscillation and with summer temperature. Heavy winter snowpack limits growth at high elevations in the western Olympics. In the warmer greenhouse climate predicted for the Olympic Mountains, productivity at high elevations of the western Olympics will likely increase, whereas productivity at high elevations in the northeastern region and potentially in low elevations of the western region will likely decrease. This information can be used to develop adaptive management strategies to prepare for the effects of future climate on these forests. Because growth–climate relationships on the Olympic Peninsula vary at relatively small spatial scales, those relationships may assist modeling and other efforts to provide more accurate predictions at local to regional scales.
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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 | 0.015 | 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) | 0.000 | 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