Status and Source Analysis of Aluminum Pollution in Xi'an Section of the Weihe River
Bibliographic record
Abstract
A preliminary study on aluminum pollution in Xi'an section of the Weihe River was carried out during 2008-2009. The result indicated that aluminum salt concentration in the Weihe River ranged from 0.10 mg/L to 4.41 mg/L, all of which exceeded the water quality criteria of both the US and Canada and could do harm to human health. In normal period of 2008, although the flow rate of the Weihe River increased from 36.4 m3/s to 81.7 m3/s after flowing through Xi'an, the aluminum concentration still rose to 0.43 mg/L from the initial value of 0.29 mg/L; and a total of 66 tons of aluminum flowed into the Weihe River from Xi'an during March 2008, of which one-fifth came from the Zaohe River. In dry period of 2009, the aluminum concentration increased by almost 1.7 times, and would result in the acute toxicity effects to surrounding aquatic lives. According to this investigation, the major cause of the increase of aluminum concentration in rivers was the flocculants used in wastewater treatment process, a great part of which were from paper mills. Since August 2008, stricter discharge standards for the paper making industry have been put into practice in China, so chemical flocculation plays an increasingly important role in wastewater treatment. All plants investigated use aluminum salts as flocculants, and this is the main reason why the aluminum concentration rose in surface waters.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".