بررسی کیفیت شاخههای جنوبی رودخانه هلیلرود براساس شاخص کیفی آب کانادا (CWQI) و نرمافزار Aquachem
Bibliographic record
Abstract
Abstract Background and purpose: Surface water, especially rivers, are one of the most important water resources that play an important role to supply water requirements of different activities. and we able to make decisions about their application with their quality monitoring. This study was done to evaluate the southern branches of Haleil Rood River quality using Canadian Water Quality Index (CWQI) and Aquachem software. Materials and Methods: In this cross sectional study, water quality parameters were used in three stations in the southern shaft of the Haleil Rood river (Hossein-Abad, Konarueyeh and Kahang-Sheibani) from 1996 to 2016. To determine the water quality of the river and determine the type and characteristics was used of the water quality index CWQI and Aquachem software Results: The results showed that water qualitative conditions in the two stations of Konarueyeh and Kahang-Sheibani are in high rank in different types of use. Hossein-Abad Station is in good condition for drinking and in terms of aquaculture in the border range and rank high for recreational activities, irrigation and livestock. Also, the analysis of the graphs obtained from Aquachem software showed that the river water of the Hossein-Abad station was in good order and the two another stations are in excellent condition. Conclusion: The cross sectional study of the chemical quality of the Haleil Rood river shows that the water river from the upstream to downstream is in excellent condition for drinking water. For agriculture, it is also within the range of high quality water. Based on the Piper diagram, the chemical quality of the river water is at the three stations studied, Sodium-Chloride. In addition based on the results, it is expected to be provided valuable information in connection with the use of water bodies by the local people of the study region.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
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".