Early Warning and Forecasting System of Water Quality Safety for Drinking Water Source Areas in Three Gorges Reservoir Area, China
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
Nowadays, the effects of water pollution accidents on water quality safety and regional residents’ living have attracted worldwide attention. Therefore, the objective of this research is to propose an early warning and forecasting model and develop a visual system of water quality safety for drinking water source areas in the Three Gorges Reservoir Area under accident conditions. Based on an Instantaneous Point Source Two-dimensional Water Quality Model and the security requirements of water quality, an early warning and forecasting model was presented, and then the system was advanced by a MATLAB platform. In addition, a hypothetical case was also carried out for the Fenghuangshan drinking water source area. Within 0.040 h to 0.096 h after the accident, the water quality could meet the standard, and the warning level was primary and intermediate, sequentially. From 0.096 h to 11.960 h after the accident, the pollutant concentration exceeded the standard, under which conditions advanced warning started. Then the intermediate and primary warnings restarted in sequence until the pollutant concentration decreased to the background value. Therefore, the proposed model could accurately predict the spatial-temporal change trend of pollutant concentration, and the developed system could efficiently realize early warnings and the forecasting of water quality safety.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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