Microbotanical residues for the study of early hominin tools
Bibliographic record
Abstract
More than 2 million years ago in East Africa, the earliest hominin stone tools evolved amidst changes in resource base, with pounding technology playing a key role in this adaptive process. Olduvai Gorge (now Oldupai) is a famed locality that remains paramount for the study of human evolution, also yielding some of the oldest battering tools in the world. However, direct evidence of the resources processed with these technologies is lacking entirely. One way to obtain this evidence is through the analysis of surviving residues. Yet, linking residues with past processing activities is not simple. In the case of plant exploitation, this link can only be established by assessing site-based reference collections inclusive of both anthropogenic and natural residues as a necessary first step and comparative starting point. In this paper, we assess microbotanical remains from rock clasts sourced at the same quarry utilized by Oldowan hominins at Oldupai Gorge. We mapped this signal and analysed it quantitatively to classify its spatial distribution objectively, extracting proxies for taxonomic identification and further comparison with freestanding soils. In addition, we used blanks to manufacture pounding tools for blind, controlled replication of plant processing. We discovered that stone blanks are in fact environmental reservoirs in which plant remains are trapped by lithobionts, preserved as hardened accretions. Tool use, on the other hand, creates residue clusters; however, their spatial distribution can be discriminated from purely natural assemblages by the georeferencing of residues and statistical analysis of resulting patterns. To conclude, we provide a protocol for best practice and a workflow that has the advantage of overcoming environmental noise, reducing the risk of false positive, delivering a firm understanding of residues as polygenic mixtures, a reliable use of controls, and most importantly, a stronger link between microbotanical remains and stone tool use.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".