Phytolith reconstruction of early to mid-Holocene vegetation and climatic changes in the Lower Yangtze Valley
Bibliographic record
Abstract
The Holocene variability of Poaceae (grasses) and their response to climate changes has not yet been investigated, despite their prevalence and biodiversity in the Lower Yangtze Valley. In this study, we analysed phytolith assemblages in a high-resolution sediment core from the Lower Yangtze Valley to reconstruct the past vegetation and climate of the early to mid-Holocene (ca. 9000–7200 cal a BP). The sediment core chronology was based on 11 radiocarbon dates of terrestrial plant macro-remains or char, which sufficiently captured regional centennial-scale vegetation and climatic variability of the early to mid-Holocene. We identified and classified phytoliths into 30 morphotypes covering seven taxonomic groups: Panicoideae, Pooideae, Bambusoideae, Chloridoideae, Oryzoideae, Arecaceae (palms), and broad-leaved trees. The downcore phytolith assemblage indicated a significant increase in palms, bamboo forests, and other evergreen broad-leaved trees after 8200 cal a BP. Both Panicoideae and Pooideae, which are indicative of dry or cold climates, decreased from 9000 to 7200 cal a BP, indicating a transition to a warmer and wetter climate during that period. Due to the coastal environment of the Lower Yangtze, both the strengthening East Asian summer monsoon (EASM) and rising sea-level likely influenced the early to mid-Holocene climate transition. The general climatic warming was interrupted by several centennial-scale intervals at 8800, 8200, 7700, and 7300 cal a BP, coinciding with weaker precipitation or lower temperature inferred from other high-resolution records; these changes were likely forced by meltwater input from the Laurentide ice sheet and solar activity. Our study provides a reliable reconstruction of climatic changes in the Lower Yangtze Valley. Moreover, the phytolith record provided a more detailed reconstruction of Holocene plant communities compared with a pollen-based record from the same core. We therefore recommend the use of phytoliths for plant reconstructions (mainly grasses) in evergreen and deciduous broad-leaved mixed forests.
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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 | 0.002 | 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) | 0.001 | 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".