Comparison of sampling strategies to monitor water quality in Prairie Watersheds
Bibliographic record
Abstract
Most water-quality monitoring programs are characterized by low-frequency sampling with variable intervals (Neal et al., 2012). In Manitoba, Conservation and Water Stewardship collects water from streams and creeks approximately four times a year with the intention of capturing seasonal water-quality fluctuations. This type of sampling is however unable to capture changes in water-quality attributes that take place at short timescales. Recent research suggests that significant fluctuations in water-quality occurr across a wide range of timescales (e.g., Kirchner, 2003; Feng et al., 2004; Kirchner et al., 2004; Halliday et al., 2012). Particularly, the diffuse transfer of nutrients in watersheds, particularly phosphorus, has been shown to occur on an hourly scale (Halliday et al., 2012). Additionally, in the Prairies, both snowmelt on frozen ground and intense thunderstorms are short-lived and tend to result in hydrological responses in a matter of hours rather than days or weeks, thus challenging the representativeness of water samples collected outside of these critical hydrological events (Zhao and Gray, 1997). The general objective of this project was to compare sampling strategies for water-quality monitoring in Prairie watersheds. The comparison was guided by three specific research questions regarding: 1. Water quality parameter sensitivity • Do specific water quality parameters range in sensitivity to hydrologic processes typical of Prairie landscapes? 2. Sampling time • Does sampling time impact the hydrochemical information obtained from water-quality analysis? 3. Data representativeness • Does an increase in sampling interval length necessarily result in the reduction of data representativeness?
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".