Evaluation of forest management effects on the mineral soil carbon pool of a lowland, mixed-species forest in Maine, USA
Bibliographic record
Abstract
Concerns about climate change have increased interest in ways to maximize carbon (C) storage in forests through the use of alternative forest management strategies. However, the influence of these strategies on soil C pools is unclear. The primary objective of this study was to test for differences in mineral soil C stocks among various silvicultural and harvesting treatments that were initiated in the 1950s and have been maintained since on the Penobscot Experimental Forest in central Maine, USA. Five mineral soil cores below the surface organic horizon to a depth of 1 m were collected from each replicate (n = 2) of selection, shelterwood, and commercial clearcut treatments. For these treatments, the mean mineral soil C stock was 47.7 ± 16.4 Mg ha−1 (mean ± SD). We found no significant differences in average mineral soil C stocks among treatments. However, a post hoc power analysis indicated that the probability of detecting a significant treatment effect was only 6%. We determined that 98 stands per treatment would be required to be 80% certain that the F test would detect a difference in average mineral soil C stocks whenever any pair of treatments had C stocks differing by more than 5 Mg ha−1.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".