Faecal indicator bacteria enumeration in beach sand: a comparison study of extraction methods in medium to coarse sands
Bibliographic record
Abstract
AIMS: The absence of standardized methods for quantifying faecal indicator bacteria (FIB) in sand hinders comparison of results across studies. The purpose of the study was to compare methods for extraction of faecal bacteria from sands and recommend a standardized extraction technique. METHODS AND RESULTS: Twenty-two methods of extracting enterococci and Escherichia coli from sand were evaluated, including multiple permutations of hand shaking, mechanical shaking, blending, sonication, number of rinses, settling time, eluant-to-sand ratio, eluant composition, prefiltration and type of decantation. Tests were performed on sands from California, Florida and Lake Michigan. Most extraction parameters did not significantly affect bacterial enumeration. anova revealed significant effects of eluant composition and blending; with both sodium metaphosphate buffer and blending producing reduced counts. CONCLUSIONS: The simplest extraction method that produced the highest FIB recoveries consisted of 2 min of hand shaking in phosphate-buffered saline or deionized water, a 30-s settling time, one-rinse step and a 10 : 1 eluant volume to sand weight ratio. This result was consistent across the sand compositions tested in this study but could vary for other sand types. SIGNIFICANCE AND IMPACT OF THE STUDY: Method standardization will improve the understanding of how sands affect surface water quality.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".