Late Holocene Vegetation and Climate Oscillations in the Qaidam Basin of the Northeastern Tibetan Plateau
Bibliographic record
Abstract
Pollen evidence from sediment cores at Hurleg and Toson lakes in the Qaidam Basin was obtained to examine vegetation and climatic change in the northeastern Qinghai-Tibetan Plateau. The chronologies were controlled by 210Pb and 137Cs analysis and AMS 14C dating. Pollen assemblages from both lakes are dominated by Chenopodiaceae (∼ 40%), Artemisia (∼ 30–35%) and Poaceae (∼ 20–25%), with continued occurrence but low abundance of Nitraria, Ephedra, and Cyperaceae. Artemisia/Chenopodiaceae (A/C) pollen ratios from two lakes show coherent large oscillations at centennial timescale during the last 1000 yr. A/C ratios were high around AD 1170, 1270, 1450, 1700 and 1920, suggesting that the vegetation was more “steppe-like” under a relatively moist climate than that during the intervening periods. Wet-dry climate shifts at the two lakes (2800 m asl) are in opposite phases to precipitation changes derived from tree-ring records in the surrounding mountains (> 3700 m asl) and to pollen and snow accumulation records from Dunde ice core (5300 m asl), showing that a dry climate in the basin corresponds with a wet interval in the mountains, especially around AD 1600. This contrasting pattern implies that topography might have played an important role in mediating moisture changes at regional scale in this topographically complex region.
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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 | 0.000 |
| 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".