Holocene seasonality changes in the central Mediterranean region reconstructed from the pollen sequences of Lake Accesa (Italy) and Tenaghi Philippon (Greece)
Bibliographic record
Abstract
This study presents pollen-based climate reconstructions of Holocene temperature and precipitation seasonality for two high-resolution pollen sequences from the central (Lake Accesa, central Italy) and eastern Mediterranean (Tenaghi Philippon, Greece) regions. The quantitative climate reconstruction uses multiple methods to provide an improved assessment of the uncertainties involved in palaeoclimate reconstructions. The multimethod approach comprises Partial Least Squares regression, Weighted Average Partial Least Squares regression, the Modern Analogues Technique, and the Non-Metric-Multidimensional Scaling/Generalized Additive Model method. We find two distinct climate intervals during the Holocene. The first is a moist period from 9500 to 7800 cal. BP characterised by wet winters and dry summers, resulting in a strongly seasonal hydrological contrast (stronger than today) that is interrupted by a short-lived event around 8200 cal. BP. This event is characterised by wet winters and summers at Accesa whereas at Tenaghi Philippon the signal is stronger, reversing the established seasonal pattern, with dry winters and wet summers. The second interval represents a later aridification phase, with a reduced seasonal contrast and lower overall precipitation, lasting from 7800 to 5000 cal. BP. Present-day Mediterranean conditions were established between 2500 and 2000 cal. BP. Many studies show the Holocene to have a complex pattern of climatic change across the Mediterranean regions. Our results confirm the traditional understanding of an evolution from wetter (early Holocene) to drier climatic conditions (late Holocene), but highlight the role of changing seasonality during this time. Our data yield new insights into the aspect of seasonality changes, and explain the apparent discrepancies between the previously available climate information based on pollen, lake-levels and isotopes by invoking changes in precipitation seasonality.
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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 | 0.001 |
| 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".