Sorghum and Finger Millet Cultivation during the Aksumite Period: Insights from Ethnoarchaeological Modelling and Microbotanical Analysis
Bibliographic record
Abstract
Cross-cultural models are a useful tool to generate hypotheses about the past using ethnographic data, especially when they can be validated against the archaeological record. In this paper, we propose the use of computer modelling techniques to gain insights into the agricultural history in the northern Horn of Africa of two key staple crops, i.e. finger millet (Eleusine coracana) and sorghum (Sorghum bicolor). To date, our understanding of the role of these cereals in the past economies of the region has been hindered by preservation issues and the limited number of systematic archaeobotanical research programs. By building predictive models that combine published ethnographic literature and environmental datasets on a global level, we can generate hypotheses about past agricultural systems in the northern Horn. The ability of the models to predict local agricultural practices in the area was tested against ethnoarchaeological observations in Gulo Makeda (Tigrai, Ethiopia). Archaeobotanical data from an archaeological site in the area, i.e. Ona Adi (ca. 750 BCE – CE 700), was used to assess the model’s predictions when applied to the archaeological record. According to our results, the rainfed agriculture of finger millet and sorghum was already in place during the Aksumite period (ca. 50 BCE – CE 800) around the main centres of settlement articulation. These results are supported by the phytolith assemblage from Ona Adi, which records the presence of water-stressed Chloridoideae and Panicoideae grasses throughout the occupation of the site.
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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 | 0.002 |
| 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 | 0.002 | 0.002 |
| 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".