Dairy Factory Wastewater from Cumulative Point of View–A Case Study
Bibliographic record
Abstract
It is needless to mention that, milk has the most appropriate and balanced combination among various foods that human feed on them daily and because of this fact milk is commonly called the perfect food. Therefore, milk and dairy products industries are one of the most important and necessary industries in all human societies. However, wastewater from this industry includes a variety of pollutants. The nature and combination of milk industry wastewater depends on the type of process being done on milk in factory and also type and combination of products that are produced in factory. In this study, the output effluent of a dairy factory was selected for investigation. Firstly, dairy wastewater specifications were introduced. Then, during 63 days, wastewater of plant was sampled ten times. Afterwards, temperature, nitrate, phosphate, BOD, COD, TSS, TDS, DO, pH, NH4, salt were measured by special devices and methods. Afterwards, by correlation analysis the impressibility of each parameter than the other variables was evaluated. The amount of these factors was compared with existing standard. Then, the correlation coefficient of these factors was evaluated by using SPSS software.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".