Riverine Water Quality Response to Precipitation and Its Change
Bibliographic record
Abstract
Surface waters are prone to the influences from both natural condition and anthropogenic activities. The aim of this paper was to study the impacts of one natural variable, precipitation, and its change posed by a changing climate on water quality of three rivers in Alberta, Canada. Eleven water quality parameters monitored during the time period of 1988–2014 were used to investigate the impact of precipitation. The results showed the significant dependence of most water quality parameters as well as river flow on the cumulative antecedent precipitation. Water quality parameters however had different associations with precipitation; and thus they would respond to climate change qualitatively and quantitatively differently in the rivers and at the stations of each river. In general, some water quality parameters such as turbidity and total phosphorus would increase; whereas other parameters would decrease or show no appreciable change under the projected increase of precipitation under the median climate change scenario for the river basins. On all three rivers, the maximum increase (17.20%) and decrease (−1.53%) were projected for turbidity and chloride, respectively, in the 2050s; while the maximum increase (29.68%) and decrease (−2.45%) were calculated for turbidity and chloride, respectively, in the 2080s. The results imply the need to manage riverine water quality considering precipitation and its change under a changing climate.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".