Comprehensive Ecological Management of Black and Smelly Open Channels: Evidence from Wuhan, China
Bibliographic record
Abstract
Despite successful initial treatment, many black and smelly waterbodies in China are polluted again, returning to the black and smelly state. To realize long-term control of water quality, this paper puts forward an integrated bioremediation method for black and smelly waterbodies, coupling nano-aeration instruments, ecological stimulators, online microbial reactors, and biological floating islands. The proposed method was applied to treat a black and smelly open channel in Central China's Wuhan City, which returned to the black and smelly state after successful initial treatment. The results show that our method reduced chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and total phosphorous (TP) by 31%, 60%, and 55%, respectively, and improved transparency and dissolved oxygen (DO) by 55%, and 180%, respectively. The water quality of the open channel was significantly improved, the smell and eutrophication were eliminated as desired, and the water quality indices all reached level V in the Environmental Quality Standards for Surface Water (GB3838-2002). To sum up, our method could gradually restore the ecology and degradation ability of rivers, kicking off a vicious cycle of the ecosystem in waterbodies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".