Successfully Dating Rock Art in Southern Africa Using Improved Sampling Methods and New Characterization and Pretreatment Protocols
Bibliographic record
Abstract
Abstract Worldwide, dating rock art is difficult to achieve because of the frequent lack of datable material and the difficulty of removing contamination from samples. Our research aimed to select the paints that would be the most likely to be successfully radiocarbon dated and to estimate the quantity of paint needed depending on the nature of the paint and the weathering and alteration products associated with it. To achieve this aim, a two-step sampling strategy, coupled with a multi-instrument characterization (including SEM-EDS, Raman spectroscopy, and FTIR spectroscopy analysis) and a modified acid-base-acid (ABA) pretreatment, was created. In total, 41 samples were dated from 14 sites in three separate regions of southern Africa. These novel protocols ensure that the14C chronology produced was robust and could also be subsequently applied to different regions with possible variations in paint preparation, geology, weathering conditions, and contaminants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".