A Comparative Study of Invasive and Micro-Invasive Analytical Methods for the Detection and Identification of Historically Applied Pesticides to Archival Records
Bibliographic record
Abstract
The National Archives UK has previously carried out invasive sampling and analysis on records in its collections which were found to contain various organochlorine pesticide (OCP) residues on all tested items regardless of format or period of creation. This study presented a rare opportunity to carry out comparative research of invasive and swab sampling methods on original, historic materials for the detection and identification of hazardous substances. Swabbing was carried out on the same records that had been invasively sampled to determine the sensitivity and applicability of this method relative to invasive sampling as well as the likelihood of pesticide transfer during handling of the records. Analysis by gas chromatography -mass spectrometry (GC-MS) demonstrated that the presence of a number of pesticides could effectively be identified using a dry swabbing method, including pentachlorophenol, ortho-phenylphenol, and the breakdown products of DDT and methoxychlor. The pesticides identified in invasive and swab testing from the same book were sometimes different, highlighting that the method of sampling will affect the results. Our results from this case study provide a snapshot, comparing the concentrations of hazardous organic pesticides detected in the invasive tests versus those found in swabs, and compare these quantities to human health-based screening concentrations. We argue that a dry swabbing programme is a good option in archival settings for screening for a comprehensive suite of pesticides, complementing existing occupational health approaches that take into account a whole collection, and can provide information to facilitate access and handling of historical records.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".