The requirements and challenges of a mobile laboratory for onsite water microbiology assessment
Bibliographic record
Abstract
Drinking water of good quality is essential to ensure the health and economical sustainability of human communities worldwide. The assessment of drinking water microbial quality is generally performed by detecting and/or quantifying faecal contamination indicators which may not provide an adequate evaluation of the health risks posed by several waterborne pathogens, for example Norovirus, Vibrio cholerae, and Cryptosporidium. In many instances, decentralized testing done in a mobile or more compact laboratory could increase the speed and capacity of predicting (or determining the source of) waterborne disease outbreaks, while offering unique opportunities to sensitize and train local populations on water and health issues. In this work, we describe the water molecular microbiology programme of the classical and molecular microbiology module of the Atlantis mobile laboratory complex, as well as the scientific, operational and design requirements that served to build a quite unique infrastructure used to study the microbial quality of drinking water in Northern Québec, Bermuda, and the Caribbean islands.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".