Nutrient cycling in a mixed-species plantation of<i>Eucalyptus globulus</i>and<i>Acacia mearnsii</i>
Bibliographic record
Abstract
A doubling of aboveground biomass production has been observed in mixtures of Eucalyptus globulus Labill. and Acacia mearnsii de Wildeman when compared with monocultures after 11 years of growth. This study examined to what extent increased nitrogen (N) availability and accelerated rates of nutrient cycling may contribute to increased growth in mixtures. Monocultures of E. globulus (E) and A. mearnsii (A) and mixtures of these species were planted in a species replacement series: 100% E, 75% E + 25% A, 50% E + 50% A, 25% E + 75% A, and 100% A. Litterfall mass increased with aboveground biomass production and was highest in 50:50 mixtures and lowest in monocultures. Owing to higher N concentrations of A. mearnsii litter, N contents of annual litterfall were at least twice as high in stands containing A. mearnsii (32-49 kg·ha1·year1) as in E. globulus monocultures (14 kg·ha1·year1). Stands with A. mearnsii also cycled higher quantities of phosphorus (P) in annual litterfall than E. globulus monocultures. This study demonstrated that mixing A. mearnsii with E. globulus increased the quantity and rates of N and P cycled through aboveground litterfall when compared with E. globulus monocultures. Thus, mixed-species plantations appear to be a useful silvicultural system to improve nutrition of eucalypts without fertilization.
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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) | 0.000 | 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".