Enumeration of Waterborne <i>Escherichia coli</i> with Petrifilm Plates
Bibliographic record
Abstract
Escherichia coli is often monitored in environmental waters as an indicator of the possible presence of human pathogens associated with feces. Petrifilm E. coli/coliform count plates (3M, Minneapolis, MN), previously validated for enumerating E. coli in food, were tested for monitoring E. coli in environmental water. Escherichia coli counts in environmental water samples enumerated with Petrifilm were significantly correlated (R > 0.9; slope = 0.9-1.0; p < 0.001) with counts obtained with three commonly used methods, mTEC (Becton Dickinson, Sparks, MD), m-ColiBlue (Hach, Loveland, CO), and Colilert-18/IDEXX Quanti-Tray 2000 (IDEXX, Westbrook, ME). Blue colonies on Petrifilm plates were most reliably identified as E. coli when accompanied by gas formation, as determined by characterization of the colonies on MacConkey agar plates (PML Microbiologicals, Mississauga, ON, Canada) and by polymerase chair reaction (PCR) with E. coli-specific primers. The main disadvantage of Petrifilm plates for environmental water testing is the small volume (1 mL per sample) that can be tested; however, the plates appear to be suitable for screening and locating sites that exceed criteria for total body and partial body contact. Simplicity of use and storage, reliability, and relatively low cost make Petrifilm plates suitable for volunteer-based and educational water quality monitoring applications, particularly when used as a preliminary screening method to identify problem sites.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".