Climate seasonality and predictability during the middle stone age and implications for technological diversification in early Homo sapiens
Bibliographic record
Abstract
Regionalisation is considered to be a hallmark of the Middle Stone Age (MSA) compared to the Early Stone Age. Yet what drove diversification around a shared technological substrate that persisted across Africa for hundreds of thousands of years remains debated. Non-mutually exclusive hypotheses include region-specific styles in manufacture, social signalling, cultural drift between geographically isolated populations, and diverse environmental adaptations, as well as the impacts of unequal research histories and intensities. We explore the potential ecological bases of behavioural diversity during the MSA between two well-studied and diverse areas: eastern and northwestern Africa. We utilise a set of standardised bioclimatic simulations, as well as a time series decomposition algorithm, to determine the nature and extent of regional differences in terms of environmental productivity, seasonality and predictability at MSA sites through time. Our results highlight that, compared to human occupations of eastern Africa, northwestern African MSA occupations are associated with colder, drier and less productive environments, albeit colder, but wetter and more productive compared to surrounding areas, with higher temperature seasonality and more predictable climates across millennia. We then theoretically consider the implications of our results for technological diversification between these two regions during the Middle to Late Pleistocene, such as for the investment in specific risk mitigation strategies for dealing with seasonally mobile resources in northern localities, and the diversification of MSA toolkits in tropical eastern Africa.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".