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>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 M alawi. Both techniques upheld the P rovenance P ostulate; 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it