Tracking the relative concentration between <i>Bacteroidales</i> DNA markers and culturable <i>Escherichia coli</i> in fecally polluted subtropical seawater: potential use in differentiating fresh and aged pollution
Bibliographic record
Abstract
Routine water quality monitoring practices based on the enumeration of culturable Escherichia coli provides no information about the source or age of fecal pollution. An emerging strategy is to use culturable E. coli and the DNA markers of Bacteroidales complementarily for microbial source tracking. In this study, we consistently observed in seawater microcosms of 3 different conditions that culturable E. coli decayed faster (T99 = 1.14 – 4.29 days) than Bacteroidales DNA markers did (T99 = 1.81 – 200.23 days). Concomitantly, the relative concentration between Bacteroidales DNA markers and culturable E. coli increased over time in all treatments. Particularly, the increase during the early stage of the experiments (before T99 of E. coli was reached) was faster than during the later stage (after T99 of E. coli was attained). We propose that the tracking of the relative concentration between Bacteroidales DNA markers and culturable E. coli provides an opportunity to differentiate a pollution that is relatively fresh from one that has aged. This method, upon further investigation and validation, could be useful in episodic pollution events where the surge of E. coli concentration causes noncompliance to the single sample maximum criterion that mandates high frequency follow-up monitoring.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".