Golin River Water Quality Assessment in Kermanshah Province Using Canada Water Quality Index (CWQI) for Construction of Fish Breeding Centers
Bibliographic record
Abstract
Rivers are considered to be among the most significant sources of fresh water, and their quality monitoring holds significant importance in terms of both spatial and temporal changes. In this study, the water quality of Golin River was investigated using the CWQI index for the construction of warm and cold-water fish breeding centers. Different parameters as: DO, pH, BOD5, NO3, Ec, turbidity, temperature, TDS, CaCO3, NO2, total hardness, CO2, Cu, NH3, and Fe were sampled twice a month from the water from April 2019 to March 2020. The results showed that the parameters of BOD5, TDS, CaCO3, NH3, Cu, and NO3 were in the unfavorable category in comparison with the standards of hot and cold-water fish farming. Comparison of CWQI index in cold-water fish farming in different months showed that the highest and lowest numerical values of the index with values of 70.02 and 60.37 were observed in February and December, respectively. Also, the numerical values of the index in warm-water fish farming were observed as the highest and the lowest numerical values of the index with the values of 85.38 and 70.96, in April and February, respectively. The results of this study showed that according to the final value of CWQI index, in general, the water of Golin river was suitable for warm-water and cold-water fish farming in the desired range.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".