Multiple paleofire proxy metrics from tropical lake sediment and soil in the Greater Serengeti Ecosystem
Bibliographic record
Abstract
Black carbon is a paleofire proxy that has been measured from glacial ice, snow, soils and lake sediments, though relatively few comparisons have been made with other fire indicators in sedimentary geoarchives. Microscopic charcoal, quantified from palynological microscope slides and macroscopic charcoal, quantified from wet-sieved deposits, are the most commonly applied methods for paleofire interpretation of Quaternary sediments. This research explores the down-profile patterns across three paleofire proxies (refractory black carbon, microscopic and macroscopic charcoal) and potential paleofire interpretations from a sediment core dating to the last centuries from Speke Gulf, Lake Victoria, and a young soil profile from a kopje located in the surrounding watershed in Serengeti National Park, Tanzania. The results of three paleofire metrics show similar trends within each site, with a positive trend across all metrics and increasing variability with increased measurement values (heteroscedastic). Notably, refractory black carbon (rBC) concentrations are two orders of magnitude higher in lake sediment samples compared to soil samples. rBC is positively correlated with both microscopic and macroscopic charcoal values and the overall profile patterns down the sediment core are similar, with the exception of the rBC increases from 2.5 to 0 cm depth that may result from increased fossil fuel combustion. The Speke Gulf rBC measurements are in an intermediate range between those published from glacial ice and other lake sediments. New rBC records from different ecosystems and temporal scales will provide paleofire insights and potential to interpret source areas and depositional patterns. The exploration of soil archives offers the potential to exploit semi-arid ecosystems and archaeological sites that have no nearby traditional paleoenvironmental study site targets.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".