Evaluation on the Drinking Water Quality Concerning Bacteria and Inorganic Nitrogen Using Ten Spring Water Samples
Bibliographic record
Abstract
Water supply self-sufficiency rate in nationwide of Japan is almost 100%. However, spring water is also used as drinking water. In this thesis, we examined bacterial contamination and inorganic nitrogen using ten spring water samples to evaluate their hygienic safety. Those samples were collected from Nov. 26, 2016 to Jan. 27, 2017. EC blue test and desoxycholate agar test were carried out for coliforms and fluorescent EC blue test was used for E. coli . Other general bacteria were detected by standard agar test. Inorganic nitrogen (e.g. NH4-N, NO2-N, NO3-N) were evaluated by using each ion selective pack test and digital pack test meter. As a result, the coliforms were detected in the range of 260 to 1 CFU/mL in five samples by desoxycholate agar tests. The results of EC blue tests in the same samples were also positive. E. coli was positive reaction in two of the five samples. Therefore, these spring water samples were judged inappropriate for drinking. In the rest five samples, there were no E. coli and no coliform. The numbers of general bacteria were detected 2100 to 0 CFU/mL. Three samples, which showed the values of 2100, 400 and 110 CFU/mL respectively, were out of the drinking water quality standard (100 CFU/ mL). The concentrations of NH4-N and NO2-N in each sample were not detected. NO3-N concentrations were the range of 40.8 to 0.27 mg/L in ten samples. Two samples (i.e. 40.8 and 21.1 mg/L) exceeded the standard quality value (NO3-N; <10 mg/L) of drinking water. In conclusion, five of the 10 spring water samples did not meet the quality standard criteria of drinking water by bacteriological examination and evaluation of inorganic nitrogen. We determined those five samples were not suitable for drinking. These methods, tried in this study, were very useful for quickly detecting the hygiene problems of spring water samples.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".