East Asian Monsoon and paleoclimatic data analysis: a vegetation point of view
Bibliographic record
Abstract
Abstract. First we review several syntheses of paleodata (pollen, lake-levels) showing the climate variations in China and Mongolia from the last glacial maximum to Present and in particular the precipitation increase at mid Holocene related to enhanced monsoon. All these results concur to a much enhanced monsoon on most of China during the first half of the Holocene. Second we present, in some details, a temporal study of a core (Lake Bayanchagan, Inner Mongolia) located in an arid region at the edge of the present East Asian Monsoon (EAM) influence and then sensitive to climatic change. This study involves pollen data together with other macro-remains and stable isotope curve to obtain a robust climate reconstruction. This study shows a long wet period between 11 000 and 5000 years BP divided in two parts, a warmer one from 11 000 and 8000 (marked by large evapotranspiration) and a cooler one more favourable to forest expansion. Third, we present a spatial study based on pollen data only and covering all China and Mongolia at 6000 years BP, but using a mechanistic modelling approach, in an inverse mode. It has the advantage to take into account environmental context different from the present one (lower atmospheric CO2, different seasonality). This study shows temperature generally cooler than present one in southern China, but a significant warming was found over Mongolia, and a slightly higher in northeast China. Precipitation was generally higher than today in southern, northeast China, and northern Mongolia, but lower or similar to today in northwest China and north China. Enhanced EAM was then found in the southern half of China and in northeast China.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".