The influence of N addition on nutrient content, leaf carbon isotope ratio, and productivity in a <i>Nothofagus </i>forest during stand development
Bibliographic record
Abstract
To test whether increased nitrogen (N) availability might increase productivity in maturing mountain beech (Nothofagus solandri var. cliffortioides (Hook. f.) Poole) forest in central South Island, New Zealand, we applied N to 25-year-old sapling and 125-year-old pole stands. Nitrogen fertilizer increased foliar and fine-root N concentrations, fine-root growth, and leaf litter production in both sapling and pole stands but had no effect on stem basal area increment or individual leaf area, and it decreased individual leaf mass marginally. Heavy flowering and seeding occurred in the second year after fertilizer was applied, and N increased production of both. Leaf litter production and flowering responded similarly to N in sapling and pole stands, but N increased fine-root and seed productivity more in pole stands than in sapling stands, confirming our hypothesis that productivity of pole stands was more limited by low N availability. Resource allocation to fine roots and seed production may have restricted stem basal area increment response to N in the short term. Pole stands had higher leaf δ13C values than sapling stands. It is concluded that both low N availability and moisture stress may contribute to the decline in productivity and wood biomass previously found in mature mountain beech stands.
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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 | 0.001 |
| 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) | 0.001 | 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".