Working toward improved monitoring of Cryptosporidium and Giardia (oo)cysts in water samples: testing alternatives to elution and immunomagnetic separation from USEPA Method 1623.1
Bibliographic record
Abstract
OBJECTIVE: This study was designed to find a method to enhance the recovery of Cryptosporidium spp. and Giardia spp. parasites from water samples for research purposes compared to the results that can be achieved with USEPA Method 1623.1. Four different approaches were used to test water samples that were artificially spiked with parasites. The approaches were: (i) Method 1623.1 itself, (ii) elution of Method 1623.1 combined with microfiltration, (iii) an elution technique based on grinding the filter membrane in a blender before the eluent was concentrated by immunomagnetic separation, and (iv) the blender elution followed by microfiltration. Fluorescence microscopy was used to determine which approach led to the highest parasite recovery rates. RESULTS: Method 1623.1 gave the best results for Giardia, while all four approaches were statistically equivalent for Cryptosporidium. We evaluated the costs and laboratory time requirements for each protocol to give readers a complete comparison of the methods tested. Elution of Method 1623.1 combined with microfiltration resulted in lower costs and less laboratory work time without compromising the recovery of the parasites.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".