Aboveground biomass and nutrient accumulation 20 years after clear-cutting a southern Appalachian watershed
Bibliographic record
Abstract
In 1975, we initiated a long-term interdisciplinary study of forest watershed ecosystem response to clear-cutting and cable logging in watershed 7 at the Coweeta Hydrologic Laboratory in the southern Appalachian Mountains of North Carolina. This paper describes [Formula: see text]20 years of change in species composition, aboveground biomass, leaf area index (LAI), and nutrient pools in the 59-ha mixed hardwood forest of watershed 7 following commercial clear-cutting in winter 1977. We measured woody species in 24 permanently marked plots before cutting in 1974 and during subsequent years (19771997). By 1997 ([Formula: see text]20 years after cutting), aboveground biomass was 81.7, 96.9, and 85.4 Mg·ha1in the cove hardwood; mesic, mixed-oak; and dry, mixed-oak communities, respectively. Leaf biomass and LAI accumulated relatively faster than total aboveground biomass in all three communities. By 1984, only 78 years after cutting, leaf biomass and LAI were nearly equal to the amount estimated for the precut forest. In 1997, nitrogen accumulation was 36, 44, and 61% and phosphorus accumulation was 48, 66, and 59% in the cove-hardwoods; mesic, mixed-oak; and dry, mixed-oak communities of the corresponding precut communities, respectively. Potassium, calcium, and magnesium accumulations were less than either nitrogen or phosphorus accumulation.
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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 | 0.001 | 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".