Bibliographic record
Abstract
The purpose of this study is to provide an accurate picture of the status of surface water quality to identify the amount of its pollution with contaminating substances so that the adoption of appropriate management practices for protecting water resources of the country at any point is determined.The objective of this study is microbial contamination of the Bahmanshir River based on water quality index in GIS environment.The method used in this study is quantitative-analytical, and to identify the impact of municipal, industrial, and agricultural wastewater and residential centers from the water outlet of the Bahmanshir River in 10 different stations, a study was conducted.River water quality parameters (fecal coliform, temperature, turbidity, PH, DO, BOD, nitrate, phosphate, Ts) in the summer and fall of 2015, in August and November in 10 to 20 of each month and three times-6 am, 12 noon and 6 pm-were studied and evaluated.Sampling, preparation, and analysis of samples were done according to standard methods (Standard Method, 1998).. Investigation showed that monthly water quality index of the river during the study period is variable in range of 22_36 and is in poor to very poor group.Water Quality Index (WQI) gradually reduces from the first station to the last station.Station 5 on the Karun River and entry of the Bahmanshir with index 22 in summer has the worst and stations 2 and 3 on the Mard Canal upstream with an index of 36 in the fall have the best status.In upstream stations of the Mard Canal, due to lack of overflow of urban, hospital, and factory sewage, the index has high quality range.In downstream station like stations 5 and 8 due to the arrival of hospital wastewater, the status of riverbank bed has changed, WQI is low, and in station 9 due to the entry of the aggregate of urban wastewater, the lowest quality index was recorded.Studying water quality in autumn and summer showed that autumn has the best status due to the start of rainfall and reduction of the pollutants, and summer has the worst status due to lack of rainfall, high temperature and evaporation, increase in wastewater, suitable conditions for coliforms growth, and increased opacity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.919 | 0.914 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".