Application of Multivariate Statistical Analysis to Define Water Quality in Jajrud River
Bibliographic record
Abstract
The copious prevalence of water deficiency and the geographical location of Iran (arid and semi-arid zone) make acquiring enough accurate data of water quantity and quality for water management vital. However, merely having sufficient data without proper interpretation is rather worthless too when it comes to effective water management and thus, there are several techniques for analyzing water quantity and quality. In this work, statistical method was used to analyze the data collected from the catchment area under study i.e. Jajrud River, located in the North West of Tehran Province. The multivariate time series method was employed to analyze water quality parameters in the river. Box-Jenkins time series model was also applied to the factor data resulted from the Multivariate time series. The results showed that the water quality parameters are not independent having a correlation coefficient larger than 0.3. The study also shows that ground water is the first effective factor, which causes increasing total dissolved solid (TDS) in the river. Domestic waste water pollution is the second-most important factor. Agricultural fertilizers and industrial waste may rank as the third and fourth pollution factors, respectively. Prediction of factor data using Box-Jenkins model was accurate and suitable which may be applicable to other place to model the factors data instead of many water quality parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".