Starch contamination landscapes in field archaeology: Olduvai Gorge, Tanzania
Bibliographic record
Abstract
No agreement on what constitutes a safe and reproducible anticontamination protocol exists for ancient starch research. Protocols applied to laboratory work may represent ‘symptomatic treatment’ only, as contamination of archaeological materials in the field may be more extensive than realized. This paper is the first systematic study on the impact that modern starches from surface and buried soils, windborne dispersal, human motion, excavation techniques and toolkits, and field attire has on archaeological sample quality. The study area is Olduvai Gorge, Tanzania. We identify seven starch types (discrete granules, n = 788) that embody the starch contamination landscape for the region. This study also demonstrates the various diagenetic changes that buried starch granules undergo in a short time, such as cavitation, fissuring, disruption and gelatinization. There are significant differences in morphotype class representation between the topsoil starches and those collected deeper below ground at excavated sites. Diagenetically transformed granules from underground storage organs dominate in soils, while native starches from cereal endosperm (Panicoideae and Triticeae) abound above ground in airborne samples. Furthermore, we illustrate how lithic samples excavated under standard field conditions can be contaminated, and that when a sample is compromised during excavation, it may be impossible to distinguish between target and introduced starches, especially when granules are identical or morphologically similar. The paper provides field recommendations to control false positives.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".