Middle Stone Age starch acquisition in the Niassa Rift, Mozambique
Bibliographic record
Abstract
Abstract The quest for direct lines of evidence for Paleolithic plant consumption during the African Middle Stone Age has led scientists to study residues and use-wear on flaked stone tools. Past work has established lithic function through multiple lines of evidence and the spatial breakdown of use-wear and microscopic traces on tool surfaces. This paper focuses on the quantitative analysis of starch assemblages and the botanical identification of grains from flake and core tools to learn about human ecology of carbohydrate use around the Niassa woodlands, in the Mozambican Rift. The processing of starchy plant parts is deduced from the occurrence of starch assemblages that presumably got attached to stone tool surfaces by actions associated with extractive or culinary activities. Specifically, we investigate starch grains from stone tools recently excavated in northern Mozambique at the site of Mikuyu; which presumably spans the middle to late Pleistocene and represents similar sites found along the Malawi/Niassa corridor that links East, Southern, and Central Africa. Starch was extracted and processed with a diverse tool kit consisting of scrapers, cores, points, flakes, and other kinds of tools. The microbotanical data suggests consumption of seeds, legumes, caryopses, piths, underground storage organs, nuts, and mesocarps from more than a dozen families. Our data suggest a great antiquity for starch use in Africa as well as an expanded diet and intensification.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".