Bibliographic record
Abstract
Introduction One of the goals of modern archeology is to understand how past communities interacted spatially, economically, and socially with their biophysical environment (Butzer, 1982). To this end, archeologists have developed strong links with zoologists, botanists and geologists to provide information on the environment of past societies and to help understand the complex relationships between culture and environment. This chapter reviews the role of diatom analysis in such studies, and discusses how the technique can be applied at a range of spatial and temporal scales to place archeological material in its broader site, landscape and cultural context. In particular, we examine applications to the provenancing of individual archeological artefacts, the analysis of archeological sediments and processes of site formation, the reconstruction of local site environments, and the identification of regional environmental processes affecting site location and the function of site networks. We have chosen a small number of examples that best illustrate these applications; other case studies directly motivated by archeological problems may be found in recent reviews by Battarbee (1988), Mannion (1987) and Miller and Florin (1989), while diatom-based studies of past changes in sea level, climate, land-use, and water quality that are also relevant to archeological investigation are reviewed elsewhere in this volume (e.g., Bradbury; Cooper; Denys & de Wolf; Fritz et al.; Hall & Smol). Analysis of archeological artefacts The direct application of diatom analysis to archeological artefacts is best represented in the field of pottery sourcing and typology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".