Spatial and temporal variability in forest growth in the Olympic Mountains, Washington: sensitivity to climatic variability
Bibliographic record
Abstract
We compared annual basal area increment (BAI) at different spatial scales among all size classes and species at diverse locations in the wet western and dry northeastern Olympic Mountains. Weak growth correlations at small spatial scales (average R = 0.084–0.406) suggest that trees are responding to local growth conditions. However, significant positive growth correlations between geographically adjacent forest types (R = 0.440–0.852) and between watersheds (R = 0.430) indicate that there is a common overarching growth-limiting factor (e.g., climate) that affects tree growth over large areas. The Sitka spruce (Picea sitchensis (Bong.) Carrière) forest type is the most sensitive to environmental change with the highest mean sensitivity (0.345), the highest potential for annual growth change (mean BAI = 0.0047 m 2 ), and the highest growth variability (coefficient of variation = 0.498). In addition, this forest type is most likely to exhibit extreme positive growth responses (4.2% of years have BAI values 2 standard deviations above the mean). Low-elevation coniferous forests are relatively sensitive to changes in growth-limiting factors (in contrast to the traditional view) and may play an important role in storing carbon in a warmer climate.
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How this classification was reachedexpand
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 | 0.023 | 0.004 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".