Somatic coliphages as bioindicators of contamination in Lake Guamuez, Colombia
Bibliographic record
Abstract
Lake Guamuez is the second largest lake in Colombia and economically supports hundreds of families in the area. The main activities carried out in the region have focused on tourism, agriculture, livestock, and rainbow trout production; however, these activities have been associated with contamination of the lake. This research aimed to evaluate the water quality of Lake Guamuez using somatic coliphages (SCs) as bioindicators. For this purpose, periodic sampling was carried out for 6 months at nine strategic points of the lake. For the detection of SCs, the method described in 9211 D of the Standard Methods for the Examination of Water and Wastewater was used. The genomic variability and presence of virulence genes in the isolated SCs were determined. Water contamination in the lake is evident, and the SCs titer is greater in areas with a high flow of anthropogenic activities. An important degree of genetic diversity and a high prevalence of virulence genes could be observed among the SCs analyzed. The results when compared with guidelines and water quality standards from various countries showed concentrations of SCs higher than those allowed. The high prevalence of gastrointestinal diseases in the region suggests a link to water contamination.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".