Age structure of aspen forests on the Uncompahgre Plateau, Colorado
Bibliographic record
Abstract
Aspen forests are one of the most dynamic forest types in western North America, responding to chronic factors of competition for resources, as well as episodes of intense herbivory, drought, and fires. The interactions of these driving factors lead to varying age structures of aspen across landscapes and through time. We characterized the age structure of aspen trees on the Uncompahgre Plateau in western Colorado, USA, to inform collaborative efforts of landscape-scale forest restoration. Over 1000 cores from 51 locations showed few aspen older than 140 years (<0.5% for aspen numbers, <2.5% of aspen basal area). Heavy recruitment in the late 1800s (following the last major fires) led to cohorts from 100 to 140 years of age that account for 15% of current aspen numbers and 40% of current aspen basal area. Perhaps the most important character of the current age structure is a relatively low number of aspen younger than 50 years; normal rates of tree survivorship in coming decades will lead to a substantial decline in aspen on the Plateau as these cohorts progress into older age classes. Patterns of aspen ages on the Uncompahgre Plateau differ substantially from those on the Kaibab Plateau and in Rocky Mountain National Park, owing the varying importance in space and time of driving factors. Landscape-scale increases in aspen regeneration (from major events such as fire) would be necessary to moderate the long-term decline in aspen on the Uncompahgre Plateau.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".