A 3000-year record of vegetation changes and fire at a high-elevation wetland on Kilimanjaro, Tanzania
Bibliographic record
Abstract
Abstract Kilimanjaro is experiencing the consequences of climate change and multiple land-use pressures. Few paleoenvironmental and archeological records exist to examine historical patterns of late Holocene ecosystem changes on Kilimanjaro. Here we present pollen, phytolith, and charcoal (>125 μm) data from a palustrine sediment core that provide a 3000-year radiocarbon-dated record collected from a wetland near the headwaters of the Maua watershed in the alpine and ericaceous vegetation zones. From 3000 to 800 cal yr BP, the pollen, phytolith, and charcoal records show subtle variability in ericaceous and montane forest assemblages with apparent multicentennial secular variability and a long-term pattern of increasing Poaceae and charcoal. From 800 to 600 cal yr BP, montane forest taxa varied rapidly, Cyperaceae abundances increased, and charcoal remained distinctly low. From 600 yr cal BP to the present, woody taxa decreased, and ericaceous taxa and Poaceae dominated, with a conspicuously increased charcoal influx. Uphill wetland ecosystems are crucial for ecological and socioeconomic resilience on and surrounding the mountain. The results were synthesized with the existing paleoenvironmental and archaeological data to explore the high spatiotemporal complexity of Kilimanjaro and to understand historical human-environment interactions. These paleoenvironmental records create a long-term context for current climate, biodiversity, and land-use changes on and around Kilimanjaro.
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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 | 0.000 |
| 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".