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 m2), 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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".