Climate, canopy disturbance, and radial growth averaging in a second-growth mixed-oak forest in West Virginia, U.S.A.
Bibliographic record
Abstract
This study evaluated the use of radial growth averaging as a technique of identifying canopy disturbances in a thinned 55-year-old mixed-oak stand in West Virginia. We used analysis of variance to determine the time interval (averaging period) and lag period (time between thinning and growth increase) that best captured the growth increase associated with different levels of crown release of Quercus prinus L. and Quercus rubra L. A lag of 3 years and an interval of 7 years yielded the best fit of percent growth change and percent crown release, respectively, for Q. prinus; for Q. rubra, the radial growth response did not differ significantly when lag and interval were varied from 1 to 3 and 6 to 15 years, respectively. The relationship between percent crown release and percent growth change was linear for both species. This method provides a suitable means of detecting canopy disturbances affecting overstory trees and is potentially applicable to other tree species. When combined with fire histories, these data can provide the basis for reconstructing long-term disturbance regimes. This estimate may also provide a framework for scheduling the rate of stand entry for silvicultural treatments (e.g., thinning) that is consistent with its historic stand development.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".