Bibliographic record
Abstract
Changes in factors such as temperature, precipitation, fire regimes, ozone, atmospheric CO2, and nitrogen deposition may have altered forest growth over the past century. Determining changes in forest growth over long intervals is complicated by constantly changing growing conditions due to tree maturation, stand self-thinning, disturbance, and other factors. Because a comprehensive review is lacking, results from publications examining forest growth trends in the western United States over the past 100 years were evaluated. Across the region, upper elevational forest ecotones have been expanding upward in many but not all locations, possibly due to warming and reduced snowpack. Across most of the region, both growth increases and decreases are localized and spatially dispersed. For the inland West, historical photography and long-term inventory data show clear densification and expansion of ponderosa pine (Pinus ponderosa) and mixed conifer forest across the region, mostly due to reduced fire incidence. However, a recent drought, probably linked to ocean cycles and exacerbated by warming, has caused a growth setback and mortality, especially in the Southwest. Forest densification due to altered fire regimes does not equate to enhanced growth per se. Aspen (Populus tremuloides) dieback has been noted due to recent drought and local conifer mortality caused by drought has also been documented in several locations. Regeneration after recent fires appears to be inadequate in many localities. Except for local increased growth at some high elevation and coastal sites and possible periodic drought effects in the Southwest, it is difficult to detect any growth trends with available data.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".