Ochre fingerprints: Distinguishing among <scp>M</scp>alawian mineral pigment sources with <scp>H</scp>omogenized <scp>O</scp>chre <scp>C</scp>hip <scp>LA–ICPMS</scp>
Bibliographic record
Abstract
In this study, we compared the effectiveness of instrumental neutron activation analysis (INAA) of bulk ochre to laser ablation‐inductively coupled plasma mass spectrometry of homogenized ochre chips (HOC LA–ICPMS) at distinguishing among three ochre sources in northern Malawi. Both techniques upheld the Provenance Postulate; however, HOC LA–ICPMS required less sample material than INAA and facilitated fast, inexpensive replicate observations that allowed for more robust statistical analysis. Our results indicated that HOC LA–ICPMS is a maturing technique that will be a valuable option for analysing artefacts that require minimally destructive sampling but are too large to fit into the laser cell for direct ablation. With regard to the statistical procedures used, stepwise canonical discriminant analysis was demonstrated to be a highly effective method for distinguishing among ochre sources, even in the presence of significant intra‐source and intra‐sample heterogeneity. Continued development of the HOC sample preparation technique will expand the range of archaeological ochre artefacts that can be included in provenance studies and prevent bias towards artefacts of convenient‐to‐analyse dimensions.
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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 | 0.000 | 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".