Tracking land-cover changes with sedimentary charcoal in the Afrotropics
Bibliographic record
Abstract
Fires have played an important role in creating and maintaining savannas over the centuries and are also one of the main natural disturbances in forests. The functional role of fires in savannas and forests can be investigated through examining sedimentary charcoal in order to reconstruct long-term fire history. However, the relationship between charcoal and vegetation structure in tropical grassy ecosystems remains to be elucidated. Here, we compared recent charcoal records from lake sediments in three tropical ecosystems (forest, savanna, and forest–savanna mosaic) with land cover inferred from remote-sensing images. Charcoal width-to-length (W/L) ratio is a good proxy for changes in fuel type. At one of the lakes, a significant W/L modification from values >0.5 (mainly wood) to <0.5 (~grass) was recorded simultaneously with changes in land cover. Indeed, a significant deforestation was recorded around this lake in the remote-sensing imagery between 1984 and 1994. The results also indicate that a riparian forest around a lake could act as a physical filter for charcoal accumulation; we used the mean charcoal size as a proxy to evaluate this process. Charcoal Accumulation Rates (CHAR), a burned biomass proxy, were combined with W/L ratio and the mean charcoal size to investigate the land-use history of the landscapes surrounding the study sites. This combined approach allowed us to distinguish between episodic slash-and-burn practices in the forest and managed fields or pastures burning frequently.
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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 | 0.001 | 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".