Slash burning, faunal composition, and nutrient dynamics in a Eucalyptus grandis plantation in South AfricaThis article is one of a selection of papers published in the Special Forum on Towards Sustainable Forestry — The Living Soil: Soil Biodiversity and Ecosystem Function.
Bibliographic record
Abstract
The sustainability of exotic commercial plantations is dependent on the conservation of soil nutrients, especially on the ancient, leached soils of the tropics, particularly when limited fertilization is practiced. In Eucalyptus grandis W. Hill ex Maid. plantations in South Africa, the site is usually burned following harvest and prior to replanting, potentially causing a disruption of soil faunal function and losses of nutrients associated with burning and removal of slash residues. The aim was to study the effect of fire on nutrient dynamics and invertebrate faunal composition. The in situ nitrogen and phosphorus mineralization rates and invertebrate faunal composition were measured in six randomly located plots — three burned and three unburned — prior to and after a low-intensity fire. Results indicate that within the burned plots, phosphorus availability was enhanced 10-fold within the first month following the fire. Invertebrate faunal diversity was low both prior to and after burning, with ants constituting the highest number. Invertebrate faunal composition was more markedly influenced by season than by the fire, with millipedes present in the majority of plots in spring and ants dominating in summer.
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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 | 0.000 |
| 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".