Comment on cp-2022-59
Bibliographic record
Abstract
Abstract. Pollen data represents one of the most widely available and spatially-resolved sources of information about the past land cover and climate of the Last Glacial Maximum (21,000 years BP). Previous pollen data compilations for Europe, the Mediterranean and the Middle East however have been limited by small numbers of sites and poor dating control. Here we present a new compilation of pollen data from the region that improves on both the number of sites (63) and the quality of the chronological control. Data has been sourced from both public data archives and published (digitized) diagrams. Analysis is presented based on a standardized pollen taxonomy and sum, with maps shown for the major pollen taxa, biomes and total arboreal pollen, as well as quantitative reconstructions of forest cover and winter, summer and annual temperatures and precipitation. The reconstructions are based on the modern analogue technique (MAT) with a modern calibration dataset taken from the latest Eurasian Modern Pollen Database (~8000 samples). A site-by-site comparison of MAT and Inverse Modelling methods shows little or no significant difference between the methods for the LGM, indicating that no-modern-analogue and low CO2 conditions during the LGM do not appear to have had a major effect on MAT transfer function performance. Previous pollen-based climate reconstructions based on MAT show a much colder and drier climate for the LGM than both Inverse Modelling and climate model simulations, but our new results suggest much greater agreement. Differences between our latest MAT reconstruction and those in earlier studies can be largely attributed to bias in the small modern calibration dataset previously used. We also find that quantitative forest cover reconstructions show more forest than that previously suggested by biome reconstructions, but less forest than that suggested by simple percentage arboreal pollen, although uncertainties remain large. Overall, we find that LGM climatic cooling/drying was significantly greater in winter than in summer, but with large site to site variance that emphasizes the importance of topography and other local factors in controlling the climate and vegetation of the LGM.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.029 | 0.013 |
| Insufficient payload (model declined to judge) | 0.259 | 0.240 |
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".