Temporal and Spatial Variation of Water Quality of Mayur River, Khulna, Bangladesh
Bibliographic record
Abstract
Mayur River, locating north western side of Khulna, has enormous significance from numerous points of views like water reservoir, navigation etc. Unfortunately latrge scale water quality degradation took place due to pollution as a result of human interruption, unplanned and untreated crude dumping of domestic, industrial and household waste into it. The aim of this study are to carry out the temporal and spatial water quality assessment of selected locations of Mayur River. The water quality was found “Very Bad” in March-2019, April-2019 and May-2019 from station 1 to station 8 except station 4 in June-2019. From July-2019 to February-2020 the water quality was found “Bad” with some exception like station 6 in February-2020. For all the stations (S1 to S8) the regression equations indicate upward regression line and the R2 values show the plotted data fits the regression model ranging 34.28% to 64.21%. Journal of Engineering Science 13(1), 2022, 89-96
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".