Carbon and nitrogen distribution and accumulation in a New Zealand scrubland ecosystem
Bibliographic record
Abstract
Reversion of agricultural land to native woody vegetation can sequester carbon (C), influencing regional and global C budgets. We examined whole-ecosystem differences in C and nitrogen (N) storage and distribution, and sapwood - leaf area relationships in a scrubland vegetation chronosequence in New Zealand dominated by manuka (Leptospermum scoparium J.R. et G. Forst) and kanuka (Kunzea ericoides var. ericoides (A. Rich.) J. Thompson). At 25 years, manuka dominated, and vegetation C was 6.5 kg C·m-2. In the 55-year-old stand, stem density was similar for the two species, and vegetation C storage was 15.1 kg C·m-2, similar to the 35-year-old stand (p = 0.9). Foliar biomass comprised 3-5% of vegetation C stock but contained 26%-37% of vegetation N. Root biomass was 10-15% of total and varied little with stand age. The sapwood - leaf area relationship differed significantly for the two species (p < 0.05). Mineral soil C and N (to 0.30 m) did not vary with stand age, but forest floor C and N were highest in the 55-year-old stand (2 kg C·m-2; p < 0.01). Soil and forest floor C/N ratios were significantly higher in the 35-year-old stand (p < 0.04), possibly because of high interspecific competition for N. While the sampling intensity was too limited to allow spatial extrapolation, our results suggest that carbon accumulation in this scrubland is rapid and similar to plantation forests, suggesting that land abandonment could significantly impact New Zealand's C budget.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".