Spatio-Temporal Characteristics and Source Apportionment of Water Pollutants in Upper Reaches of Maotiao River, Southwest of China, from 2003 to 2015
Bibliographic record
Abstract
The Maotiao River is playing an indispensable role in protecting water quality of the Yangtze River of China. Its hydropower development also provides adequate power and clean resources for the local areas. To understand the water quality of the upper reaches (i.e., Maijia River), seven indices such as dissolved oxygen (DO), chemical oxygen demand (CODCr), biochemical oxygen demanded (BOD5), ammonia nitrogen (NH3-N), total nitrogen (TN), total phosphorus (TP) and fluoride of samples collected from 4 sites from 2003 to 2015 were studied using multiple analysis approaches. For winter-spring and summer-autumn seasons, pictures of spatio-temporal characteristics were presented and the reasons behind their variation trend were elaborated. The Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) was evaluated to concisely mark the water quality. Principal component analysis (PCA) was applied to identify the source of pollutants. The results showed that the water quality status in Maijia River was poor from 2008 to 2011 and acceptable from 2003 to 2007, and 2012 to 2015, respectively. The CODCr, NH4-N and TN were considered to be the primary pollutants during winter-spring and summer-autumn seasons. The quality of Maijia River was influenced strongly by human activities. Environmental treatment and pollution sources of the middle and lower reaches of the river need to be focused. This study paves a way to improve the ecological environment of Maotiao River and overall water quality management of the middle and upper reaches of Yangtze River.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 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".