The impact of climatic factors on bacterial indicators of freshwater quality
Bibliographic record
Abstract
Most of microbial pathogens present in natural waters are of fecal origin. Thus detection of fecal contamination is the main aim of water quality monitoring. Routine bacterial indicators of fecal contamination are: the coliforms, fecal coliforms, fecal streptococci, Clostridium spp and Pseudomonas aeruginosa. The present study aimed to determine the impacts of climatic factors on concentrations of these bacterial indicators and evaluate their assesability for biomonitoring of natural freshwater ecosystems. The study was conducted on the river Neretva, a karst river in southeastern Bosnia and Herzegovina. The water was sampled at seven selected sites at bimonthly intervals, from October 2015 to October 2016. Bacterial counts were found to be high during all tested seasons. All indicators showed a similar pattern of concentrations with the highest counts during summer. The counts were also increased in winter, while the decrease of values of all tested parametres was noted in spring. The lower water temperatures were not shown to cause a significant decrease of bacterial counts. Seasonal variations were pronounced, however data should be interpreted with caution whilst taking into account natural climatic oscillations and hydrological characteristics. Further studies should examine correlations between fecal indicator bacteria and possible concurrence with other pathogens.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".