Recommendations for stable isotope analysis of charred archaeological crop remains
Bibliographic record
Abstract
Stable isotope analysis of plant remains recovered from archaeological sites is becoming more routine. There remains a lack of consensus, however, on how to appropriately select archaeological plant remains for isotopic analysis, how to account for differences in preservation and the effect of potential contamination, and how to interpret the measured isotope values in terms of the conditions in which the plants grew. In this paper, we outline the main issues to be considered when planning and conducting an isotopic study of archaeobotanical remains. These include: (1) setting out the research question(s) that will be answerable using available analytical approaches, (2) considering the archaeological context from which plant remains derive, (3) determining appropriate sample size through consideration of estimate precision, (4) establishing the conditions in which plant remains have been preserved and potential effects on their isotope values, and (5) accounting for possible contamination during deposition. With these issues in mind, we propose some recommendations for researchers to consider when planning and conducting an isotopic study of archaeobotanical remains.
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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 | 0.054 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.016 |
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".