Variation of an indicator of
Bibliographic record
Abstract
Escherichia coli is an indicator of fecal pollution used to mandate recreational and drinking water quality. Concentrations of culturable E. coli following contamination of surface water are determined by three factors: dilution; cell attachment to particulate material and settling or resuspension in the water column; and the net rate of change in viability. This study evaluated the variability in the latter parameter, and how predictive variation in death rate was of culturable population densities at the time of sampling. Water samples (N=232) with varying levels of E. coli contamination were collected from 46 discrete locations in four watersheds across Canada over a three-month period and enumerated for culturable E. coli by membrane filtration plate counting (T0EC). Water samples were again enumerated following a laboratory 24 h holding period at 30°C in the dark, and the difference considered the death rate (ΔEC). Relationships of T0EC and ΔEC with environmental and water chemistry factors were explored using step-wise multiple regression. The model predicting T0EC indicated that stream order, total rainfall seven days in advance of sampling day, total phosphorus, and ΔEC were the most significant contributors. The model predicting ΔEC indicated that turbidity and NH3+NH4 were the most important contributors. A model suggests that the persistence factor is less important than dilution (i.e. stream order) in describing E. coli densities, followed by factors that influence the loading of E. coli into watersheds.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".