Appropriate Discharge from Diversion Dam to Dilute High Concentrated Community Wastewater of Riverbank Settlements along Phetchaburi River in Phetchaburi Province, Thailand
Bibliographic record
Abstract
The research was focused on determining dilatability of traveling mass water from Phetchaburi diversion dam on high concentrated community wastewater that flowing into Phetchaburi river. The mass water quality was sampled at six-consecutive measuring stations from Phetchaburi diversion dam to the river mouth with various distances of traveling, and analyzed for 27 environmental indicators before employing ANOVA ANALYSIS which resulted with highly significant differences between indicator and station only BOD, COD, TSS, and turbidity. When the linear regression was employed to determine both the water quality indicators in relation to discharges (10, 15, 20, and 25 cms.) and the traveling distances, resulting the determination coefficients of 0.93 for BOD, 0.80 for COD, 0.83 for TSS, and 0.50 for turbidity. Only BOD value is included in the list of surface water quality standards. After employing the derived equations to calculate BOD in comparison with observed value, then it was chosen to support the decision of required BOD for 3 mg/L at Muang Municipal bridge together with discharge of 10-15 cms from Phetchaburi diversion dam. If more or less this range in summer period, the dilatability of high contaminant concentration was decreased 2-10 folds due to less mass water flow and washing off organic and solid wastes from flooding along the riverbanks. Furthermore, the traveling mass water from diversion dam is possibly eligible to dilute the high concentrated wastewater from dense and populated community but the diluted mixture depending on the amount and concentration of wastewater inflow.An abstract is a brief, comprehensive summary of the contents of the article, it allows readers to survey the contents of an article quickly and, like a title, it enables persons interested in the document to retrieve it from abstracting and indexing databases. Most scholarly journals require an abstract. Consult the instructions to authors or web page of the journal to which you plan to submit your article for any journal-specific instructions. A well-prepared abstract can be the most important single paragraph in an article. Most people have their first contact with an article by seeing just the abstract, usually in comparison with several other abstracts, as they are doing a literature search. Readers frequently decide on the basis of the abstract whether to read the entire article. The abstract needs to be dense with information. By embedding key words in your abstract, you enhance the user’s ability to find it. Do not exceed the abstract word limit of the journal to which you are submitting your article. Word limits vary from journal to journal and typically range from 150 to 250 words. For information on how abstracts are used to retrieve articles, consult Record Structure for APA Databases
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".