Online appendix 1:A geomorphic and soil description of the long-term fire experiment in the Kruger National Park, South Africa
Bibliographic record
Abstract
In 1954, the experimental burning programme into fire research was initiated in the Kruger National Park (KNP), South Africa. It is viewed as one of the last remaining long- term landscape fire experiments in Africa. Throughout the more than five decades of fire treatments in the experiment, numerous surveys (expanding various spatial and temporal scales), research projects (covering biotic and abiotic components) and analyses have been conducted with the aim to assess the impacts of different fire regimes on the savannah biome. The design of the experiment intended to test the effect of season and frequency of burning on vegetation within four major landscapes in the KNP. However, these effects have been partly obscured by factors not fully taken into account by the experimental design, namely, herbivory, artificial water provision and soil variation. Soil variation between replicates in the same landscape, as well as within individual replicates, has raised the issue of the representivity of the trial. This paper provided a description and ranking of the experimental burning trial according to the geomorphic and soil characteristics of each plot in comparison to the surrounding landscape.Conservation implications: The KNP burn plots are one of the largest and longest-running fire experiments on fire ecology in African savannahs. However, studies need to consider the underlying geomorphic and soil template when designing experiments and interpreting results. This work describes the representivity of the plots across, and within, treatments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.389 | 0.104 |
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".