Effects of timber harvest on carbon pools in Ozark forests
Bibliographic record
Abstract
We quantified and compared carbon (C) pools at a Missouri Ozark experimental forest 8 years after different harvest treatments. Total C pools were 182, 170, and 130 Mg C·ha–1for the control (no-harvest management; NHM), single-tree, uneven-age management (UAM), and clearcut even-age management (EAM) stands, respectively. Harvesting reduced the live tree C pool by 31% in the UAM, 93% in EAM stands, and increased the coarse woody debris (CWD) C pool by 50% in UAM and 176% for EAM, compared with NHM stands. UAM significantly (p = 0.02) increased the mineral soil C pool by 14%, whereas EAM had no effect. More interestingly, the distribution of C among various components (i.e., live, dead wood, CWD, litter, and soil) ranged from 0.7% to 29% on NHM stands and from 0.1% to 43% on EAM stands. Soil nitrogen (N) (%) was significantly correlated with soil C (%) in the UAM stands, whereas soil temperature was negatively related to live tree C. Soil N (%) and canopy cover were significantly correlated with live tree and soil C (%) pools at EAM stands. Our results revealed that the largest C pool in these forests was living trees. The soil and CWD C pool sizes suggest the importance of dynamics of decaying harvest debris, which influences N retention.
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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 | 0.000 | 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".