Seeing the Wood for the Trees: Recent Advances in the Reconstruction of Woodland in Archaeological Landscapes Using Pollen Data
Bibliographic record
Abstract
This paper reviews recent advances in the reconstruction of woodland cover from palynological data. Pollen sequences record the vegetation cover of past landscapes, but translating a pollen diagram into a landscape reconstruction is not straightforward. This paper focuses on the use of pollen records to address three archaeologically relevant problems, the detection of woodland presence and extent in a largely open landscape, the reconstruction of the habitat context of a specific archaeological site, and the detection of woodland management. Research seeking to quantify past land-cover using models of pollen dispersal and deposition has led to the development of algorithms and computer software linking maps of the arrangement of land-cover with simulated pollen records at possible coring points. This software can be used to carry out thought experiments and test competing hypotheses, and also underpins the Multiple Scenario Approach to the reconstruction of past land-cover. Modern datasets of pollen surface samples and associated vegetation abundances are needed to calibrate these models, and can also provide insights into how the pollen record ‘sees’ landscapes, and therefore aid interpretation of past pollen records. We demonstrate how simulation approaches and surface sample studies are improving the scientific basis of reconstruction of past landscapes, and how these approaches offer new opportunities for communication and collaboration between archaeologists and environmental specialists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".