Semi-automated water sampling module for repeated sampling and concentration of Bacillus cereus group spores
Bibliographic record
Abstract
Monitoring the presence of pathogenic Bacillus spores is important for industrial applications, as well as necessary for ensuring human health. Bacillus thuringiensis is used as a biopesticide against several insect pests. Bacillus cereus spores are a significant cause of food poisoning, and Bacillus anthracis is a recognized biosecurity threat. Laboratory-based methods, such as polymerase chain reaction, enzyme-linked immunosorbent assay, or matrix-assisted laser desorption ionization spectroscopy provide sensitive detection of bacteria and spores, but the application of those methods for quasi-continuous environmental monitoring presents a significant challenge requiring frequent human intervention. To address this challenge, we developed a workstation for quasi-autonomous monitoring of water reservoirs for the presence of bacteria and spores, and designed and validated the functionality of a microprocessor-controlled module capable of repetitive collection and pre-concentration of spores in liquid samples tested with fiberglass (FG), polyether sulfone and polyvinylidene fluoride filters. The best results were obtained with FG filters delivering a 20× concentration of B. thuringiensis and B. cereus spores from saline suspensions. The successful 20× pre-concentration of Bacillus spores demonstrated with FG filters could be repeated up to 3 times when bleach decontamination is applied between filtrations. Taken together, our results demonstrate an attractive instrument suitable for semi-automated, quasi-continuous sampling and pre-processing of water samples for biosensing of bacterial spores originating from a complex environment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".