Dynamics of coarse woody debris following gap harvesting in the Acadian forest of central Maine, U.S.A.
Bibliographic record
Abstract
We examined the dynamics of down coarse woody debris (CWD) under an expanding-gap harvesting system in the Acadian forest of Maine. Gap harvesting treatments included 20% basal area removal, 10% basal area removal, and a control. We compared volume, biomass, diameter-class, and decay-class distributions of CWD in permanent plots before and 3 years after harvest. We also determined wood density and moisture content by species and decay class. Mean pre-harvest CWD volume was 108.9 m3/ha, and biomass was 23.22 Mg/ha. Both harvesting treatments increased the volume and biomass of non-decayed, small-diameter CWD (i.e., logging slash), with the 20% treatment showing a greater increase than the 10% treatment and both treatments showing greater increases than the control. Post-harvest reduction of advanced-decay CWD due to mechanical crushing was not evident. A mean of 18.48 m3 water/ha (1.85 L/m2) demonstrates substantial water storage in CWD, even during an exceptionally dry sampling period. The U-shaped temporal trend in CWD volume or biomass seen in even-aged stands may not apply to these uneven-aged stands; here, the trend is likely more complex because of the superimposition of small-scale natural disturbances and repeated silvicultural entries.
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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 | 0.000 | 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".