Water quality response to river flow regime at three major rivers in Alberta
Bibliographic record
Abstract
Abstract Both anthropogenic activities and natural factors affect river water in quantity and quality, while anthropogenic activities have been often blamed to cause water quality temporal degradation. In addition, riverine water quality displays intra-annual/seasonal variations, which are often more prominent than inter-annual variations. The intra-annual variations in water quality, which are attracting the attention of managers and policy-makers, beg the question of how to better manage riverine water quality at a finer time resolution. The natural factors, in particular, the hydro-meteorological variables, could be the primary drivers of the intra-annual variations of riverine water quality. Therefore, this paper examined the association between riverine water quality and one hydro-meteorological variable (flow) with the focus on their relationship at the intra-annual timescale on three selected rivers in Alberta, Canada. The results demonstrated that flow drives intra-annual variation of riverine water quality in general. Moreover, some water quality parameters responded to flow distinctively at three flow regimes (low, medium, and high flows). Water quality parameters were categorized into eight groups according to their responses to flow at the flow regimes. These implied the challenges in water quality management while providing insight into how to formulate more effective water management strategies.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".