Results from an Australopithecus africanus dental enamel fragment confirm the potential of palaeoproteomics for South African Plio-Pleistocene fossil sites
Bibliographic record
Abstract
The southern African Late Pliocene to Early Pleistocene hominin record is abundant and exhibits a high taxonomic diversity with three genera represented: Australopithecus, Paranthropus and Homo. Hominin fossil diversity and variation are often contextualised within other fossil assemblages or modern/extant counterparts. However, the incompleteness of the fossil record, sample selection bias and taphonomic condition of the specimens themselves constrain interpretations of diversity and variation within and between species. Thus, species identification and the nature of the observed variation are frequently debated. Palaeoproteomics can help improve our understanding of taxonomic variation, as demonstrated by the recently generated proteome of Paranthropus specimens from Swartkrans. Here, we demonstrate protein preservation for an A. africanus specimen from Sterkfontein Member 4, Sts 63, using minimally invasive analysis, and identify it as belonging to a male individual. We then discuss some of the current limitations of palaeoproteomics and how we can potentially overcome them. Although it is still in its infancy for Plio-Pleistocene hominin fossils, palaeoproteomics has the potential to help unravel the causes of observed morphological variation. Lastly, we strongly believe that the involvement of African researchers at all levels of this research, including leadership, is of great importance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".