Evaluation of microbial indicators to distinguish sources of pollution in groundwater.
Bibliographic record
Abstract
In this study three different Microbial Source Tracking methods (MST) have been evaluated in laboratory experiments with the objective of determining which is more effective and reliable in distinguishing between human and animal sources of fecal contamination in groundwater. A preliminary literature review of all the MST methods applicable for surface and ground water has been done, and the use of microbiological indicators has been selected. The methods selected involved the enumeration of Fecal Coliforms (FC), Fecal Streptococci (FS), Clostridium perfringens (CP) and Human Bifidobacteria (SFB), parameters which are generally defined as Bacterial Indicators (BI). The first method consisted of calculating the FC/FS ratio, the values of which can be related to animal or human sources, while the second method consisted of the enumeration of CP, which in some studies was associated with animal sources of fecal pollution. Finally, the third method enumerated levels of SFB, which are associated exclusively with human sources of fecal pollution. (Abstract shortened by UMI.)Dept. of Civil and Environmental Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .C565. Source: Masters Abstracts International, Volume: 43-01, page: 0289. Advisers: H. Biswas; A. Hubberstey. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".