The Global Pollen Database in biogeographical and palaeoclimatic studies
Bibliographic record
Abstract
The Global Pollen Database is an example of a successful data synthesis effort that has uses for biogeographical and climate change studies. Results are of interest in many fields of physical geography. Continental-scale maps of past conditions have been used in data-model comparison studies. Time series, developed by averaging quantitative reconstructions from many sites, have indicated that millennial-scale climate variability has affected the vegetation of Europe and North America during the Holocene. Major transitions in the vegetation of Europe and North America occurred at the same time, suggesting the overriding climate effect on the vegetation of both continents. The database can also be used to test biogeographical hypotheses, as several examples illustrate, without the need for collecting new data. Hundreds of studies over the past 50 years show that pollen analysis is more precise than frequently acknowledged: vegetation responds rapidly to climate variations, changes in vegetation are spatially coherent and the taxonomic resolution available in the database is greater than frequently acknowledged. The availability of a public, freely available database enables different analyses to be performed on the same data, thereby ensuring that results are not dependent on methodology.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".