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Recovery of DNA from Exhibits Contaminated with Chemical Warfare Agents: A Preliminary Study of the Effect of Decontamination Agents and Chemical Warfare Agents on DNA

2007· article· en· W2067227854 on OpenAlexaffvenue
D. Adrian Wilkinson, D Sweet, Diane Fairley

Bibliographic record

VenueCanadian Society of Forensic Science Journal · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicForensic and Genetic Research
Canadian institutionsVancouver Enterprise ForumRoyal Canadian Mounted Police
Fundersnot available
KeywordsChemistryHuman decontaminationPhosgeneChlorineSulfur mustardChemical Warfare AgentsChromatographyDNA damageChemical warfareSulfurSarinDNABiochemistryOrganic chemistryToxicityMicrobiologyBiologyMedicine

Abstract

fetched live from OpenAlex

Bloodstains were exposed to chemical warfare agents and then to decontamination agents to evaluate the effect on DNA extraction and profiling for human identification. DNA profiles were not obtained from blood dilution samples exposed to the decontamination agents CASCAD® and the American equivalent, MDF LSA-100, even when quantifiable DNA was extracted. Fresh and aged bloodstains were exposed to nine chemical warfare agents: hydrogen cyanide, phosgene, chlorine, dimethyl sulfate, sodium fluoroacetate, diazinon®, sulfur mustard, lewisite I, and sarin in gaseous and/or liquid form. In general, following exposure to chemical warfare agents, most bloodstains gave lower concentrations of DNA relative to controls. Full DNA profiles were obtained from bloodstains exposed to sarin, diazinon®, sodium fluoroacetate, and hydrogen cyanide. During exposure to chlorine, phosgene, lewisite I, and dimethyl sulfate, the bloodstains changed colour from red to brown. These four chemical warfare agents were shown to inhibit the recovery of DNA. Although samples exposed to sulfur mustard did not exhibit any colour changes, recovery of DNA was inhibited in some samples exposed to sulfur mustard.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.268
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2007
Admission routes2
Has abstractyes

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