Composite <i>versus</i> single sampling of spent irrigation water to assess the microbiological status of sprouting mung bean beds
Bibliographic record
Abstract
Abstract BACKGROUND: Spent irrigation water testing has been recommended in sprouted seed production to detect the presence of pathogens. However, because of the heterogeneous distribution of contamination within batches of sprouted seed, taking single samples of spent irrigation water may return false‐negative results. The following evaluated whether spent irrigation water collected from multiple points provided a more representative assessment of the microbiological status of the sprouting mung bean bed compared to when single samples were taken. RESULTS: Generic Escherichia coli or Aeromonas was recovered in one and 10 of the 160 sprout samples taken from 32 sprouting mung bean batches, respectively. Composite spent irrigation water samples tested positive for generic E. coli on 19 occasions compared to 12 when single samples were taken. Mesophilic Aeromonas was detected in 13 composite spent irrigation water samples which compared to eight single samples. The prevalence of either target bacterium in composite spent irrigation water samples was not significantly (P > 0.05) different compared to when a single sample was collected. CONCLUSIONS: Sampling spent irrigation water from multiple points under sprouting mung bean beds does not significantly increase the probability of detecting contamination, if present. The findings of the study should be considered when devising sampling plans for spent irrigation water testing in bean sprout production. Copyright © 2008 Society of Chemical Industry
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".