Growth response of ponderosa pine (<i>Pinus ponderosa</i>) to climate in the eastern Cascade Mountains, Washington, U.S.A.: Implications for climatic change<sup>1</sup>
Bibliographic record
Abstract
Climatic change is expected to cause dramatic shifts in low-elevation treeline in mountainous environments. Ponderosa pine (Pinus ponderosa) were sampled across an elevation gradient adjacent to the Methow Valley of the Okanogan National Forest in the eastern Cascades, Washington to examine the potential response of ponderosa pine to climatic change. Response function analyses were used to compare climate-growth relationships among 12 sites, four elevations on three different mountains. Response function analysis attributes 42-55% of the inter-annual variation in growth to climate. Growth is positively correlated with November precipitation prior to the growing season on all 12 sites, suggesting that November precipitation is critical for increased root growth, increased nutrient availability through decomposition, building snowpack, or non-growing season photosynthesis and carbon storage. Growth is positively correlated with previous October, January, June, and July precipitation at more than one site. Temperature is not correlated with growth on any sites. Climate models predict that the Pacific Northwest will experience warmer and wetter winters and drier summers in the future. Growth-climate correlations suggest that the short-term growth response of ponderosa pine is most sensitive to non-growing season precipitation. Therefore, predicting ponderosa pine’s response to projected climatic change is problematic, with wetter falls increasing growth and drier summers decreasing growth. Our results indicate that ponderosa pine is much more sensitive to precipitation than temperature and that any predictions of this arid species’ response to climatic change are difficult, due to uncertainty in predicting future precipitation patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".