Fire dynamics and driving mechanisms on the Eastern Coast of China since the Late Pleistocene: evidence from charcoal records on Shengshan Island
Bibliographic record
Abstract
Fires play a significant role in ecosystems, exerting a profound influence on climate, vegetation, and geochemical cycles, while being reciprocally affected by these factors. The reconstruction of past fire events serves as a valuable window into understanding environmental changes over time. To investigate the history of ancient fires on the Eastern Coast of China, we conducted the first charcoal analysis on a loess profile of Shengshan Island (East China Sea). Along with other biological and geochemical proxies, we successfully reconstructed the ancient fire dynamics and elucidated their driving mechanisms in this region since the Late Pleistocene. Our initial findings revealed a peak in charcoal concentration during the 60-50 ka period, but after calibrating for sedimentation rate, the concentration significantly decreased. Fire activities remained weak during 50-30 ka, likely due to the scarcity of combustible materials. Between 30-12 ka, fires were frequent in the early period, while gradually diminishing during the later stage. Dry climate and dense vegetation likely attributed to frequent fires in early period, while some extreme events (e.g., sudden change in temperature) may have decreased the fire frequency in later period. The Holocene (began ~12 ka) evidenced the most frequent fire events as a high charcoal concentration was recorded, likely caused by human activities. After comparing our findings with other paleoecological records from surrounding areas, we confirmed the accuracy of our reconstruction of ancient fires. This reconstruction captures not only local shifts but also broader regional changes. Overall, our study highlights the importance of calibrating sedimentation rate in charcoal profiles, while also contributing to an enhanced understanding of environmental changes along the Eastern Coast of China since the Late Pleistocene.
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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 | 0.002 |
| 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) | 0.000 | 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".