Verification of Cau River biochemical water quality forecasted from local governments’ socioeconomic projections in Bac Kan and Thai Nguyen provinces, Vietnam
Bibliographic record
Abstract
In this study, we made a verification of water quality between forecasted and monitoring data in 2015 to find out the differences for Cau River water quality, BOD5 concentration (5-day Biochemical Oxygen Demand). Then, an analysis on main development factors which may cause these differences was made. The results showed that in general, BOD5 monitoring concentrations are lower than forecasted median results and meet the standard QCVN 08-MT:2015/BTNMT for surface water quality, while the forecasted concentrations in some periods are over the standard. Local government’ control of population growth in Bac Kan City is considered as one of the major reasons to make Cau River water quality better than forecasted. The statistic population data of Bac Kan City in 2015 is lower than the forecasted population: 26.19% lower than the forecasted in the low population growth scenario (S1), 28.04% in the medium population growth scenario (S2), and 29.8% in the high population growth scenario (S3). Besides, the operation of domestic wastewater treatment plant in Cho Moi Town, which was not considered in developing and assessing the impacts of population scenarios on water quality, is also considered as one of the reasons why the Cau River monitoring water quality (BOD5 concentration) is better than forecasted. This verification result is important and useful for increasing the quality of scenario development and water quality forecast in the future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".