Temporal and Spatial Variation of Water Quality of Mayur River, Khulna, Bangladesh
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it