Bibliographic record
Abstract
Abstract. Temperature and mineral dust records serve as valuable paleoclimate indicators for studying atmospheric variability across a wide range of temporal scales. Due to the typically lower resolution of older sections within these records, studies investigating the geographical variability of the atmosphere have predominantly focused on periods shorter than one glacial cycle, such as the Holocene or the Last Glacial Maximum. In this study, we utilise a Haar-based algorithm to evaluate the geographic variability of dust and temperature records throughout the last glacial cycle. This algorithm enables us to analyse non-equidistant sampling series, allowing for the utilisation of both high and low-frequency information from the records. Consequently, we can investigate timescales ranging from decades to thousands of years. Notably, our findings indicate that the transition from macroweather to climate regimes occurs at shorter timescales in polar regions compared to the tropics or mid-latitudes. Furthermore, disparities between the dust records of the North and South Poles were observed. Finally, we assess the time-dependent correlation between the polar regions and the lower latitudes. Our analysis reveals high correlations at timescales of approximately 20, 40, and 100 kyr, which aligns with the Milankovitch cycles. Conversely, all sites exhibit a loss of correlation between 40 and 80 kyr, indicating the absence of an identifiable oscillation synchronisation mechanism at these scales. On the one hand, our findings support the use of the Haar-based method as an alternative for analysing nonuniform datasets. On the other hand, they underscore the necessity for additional high-resolution or longer time series data from the tropics or mid-latitudes, as the currently available data fail to adequately represent the glacial-interglacial cycles.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.263 | 0.173 |
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".