Evaluation of the Effects of Radiation from an X-ray Baggage Inspection System on Microbial Agents
Bibliographic record
Abstract
During shipping, microbial cultures and clinical samples are subjected to irradiation by x-ray baggage inspection systems and most high-containment laboratories use similar equipment to screen materials prior to admittance. Low-to-medium kiloelectron volt (keV) energy baggage x-ray inspection systems are used for this purpose. However, the effect of the x-ray exposure that occurs during the screening session on the viability of microbial agents or the radiation's ability to induce damage to their genomes is unknown. This study was undertaken to determine if the x-ray screening process has any deleterious effects on microbial viability or if it causes mutations to their genome. A total of 11 microorganisms, including bacteria, bacterial spores, yeast, and viruses, were screened with a baggage x-ray inspection system. No evidence of loss of viability was observed. The Ames test was used to determine the extent of radiation-induced mutations resulting from a baggage inspection system exposure. This study concludes that low-to-medium energy x-ray radiation received from the baggage screening x-ray inspection systems used in security operations does not significantly reduce the viability of microorganisms nor cause mutations in the microbial genome.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".