Oddy Tests: Adding the Analytical Dimension
Bibliographic record
Abstract
Abstract The so-called “Oddy test” has gained popularity in art and archaeology collections because it is inexpensive to use, and the results are relatively easy to analyze. However, the method is also subjective and does not identify the pollutants. Here we present a modification to the traditional test that addresses these drawbacks and aims at providing solutions. Activated carbon was used to adsorb the volatile emissions generated within the standard Oddy test setup. Gas chromatography–mass spectrometry (GC-MS) analysis of the volatile organic compounds (VOCs) desorbed from the activated carbon detected pollutants within a considerably shorter time frame than the traditional Oddy test and provided both qualitative and quantitative data. GC-MS analysis of volatiles off-gassed during the Oddy test provided information about the VOCs from local brands of materials commonly used for conservation and storage of objects in collections. The use of GC-MS analysis of volatiles improves the Oddy test in a fast, sensitive, and quantitative manner.
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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 | 0.019 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".