Thinning and site quality influence aboveground tree carbon stocks in yellow-poplar forests of the southern Appalachians
Bibliographic record
Abstract
Little information exists regarding the effects of intermediate stand management activities (e.g., thinning) on C storage. This lack of information has created uncertainty regarding trade-offs between the benefits observed following thinning and C storage. Using long-term growth data, this study examines the effect of thinning on C storage while controlling for the effects of site quality in yellow-poplar ( Liriodendron tulipifera L.) forests throughout the southern Appalachian Mountains. In 1960, one hundred and eighteen 0.1 ha plots were established in yellow-poplar forests throughout the southern Appalachians and subsequently thinned to a randomly assigned residual basal area (RBA) (square metres per hectare). Carbon storage increased through time across all levels of RBA. RBA had a long-term effect on C storage with greater C storage occurring at greater RBA. On average-quality sites, thinning to 30 m2·ha–1stored 84% more C than thinning to 10 m2·ha–1. At no time did plots with progressively lower RBA store more C than plots with progressively higher RBA. The results from this study provide information about the effects of intermediate silvicultural disturbance on C dynamics of the aboveground live tree pool in a complex landscape and may be used to inform decisions regarding trade-offs between active management and C storage.
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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 | 0.001 |
| 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".