Impacts of management on ecosystem service capacity in northeastern U.S. Appalachian forest stands
Bibliographic record
Abstract
Forests are critical resources that provide multiple ecosystem services. However, the capacity to produce these services depends on how forests are managed. The northeastern US is a highly forested and densely populated area with a strong potential for delivering multiple ecosystem services to owners of small, private forestlands. Thus, our objective was to quantify the impacts of harvesting on small forest stands in a northeastern US Appalachian forest. We leveraged existing harvest and inventory data to assess how harvest intensity, harvest type, and time since harvest completion play a role in forest productivity and biodiversity changes over time. In general, lower harvest intensities and longer times since completion improved forest stand ecosystem capacity. Harvesting at intensities of 30% or less of biomass removal and time durations of at least 15 years post-harvest allowed stands to recuperate carbon losses and improve biodiversity metrics. These results align well with previous recommendations of lower harvest intensities and longer rotations, and also suggest that co-production of multiple ecosystem services has potential for small forestland owners to manage their resource sustainably.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".