Characterization of natural and environmental flows in New Brunswick, Canada
Bibliographic record
Abstract
Abstract A good understanding of the natural flow regime plays an important role in many hydrological studies. Also important in such studies is the quantification of environmental flows. This study focuses on flow metrics that best describe the natural flow regime and the hydrological characteristics for rivers in New Brunswick (Canada) as well as quantifying environment flows for these rivers. New Brunswick rivers have a mean annual flow (MAF) of approximately 23 L s−1 km−2, which is also reflective of the water availability. The frequency analysis showed that low flows (T = 2–50 years, where T is the recurrence interval) were all below the 10% MAF. Environmental flow methods based on the MAF and flow duration analysis (median flow) showed good regional regression equations. However, flow duration methods showed high variability especially at flows between Q80 and Q100. Flow targets based on the 25% MAF, Q50 and 70% Q50 were used to estimate environmental flows, particularly during low‐flow periods (winter and summer). Results showed that the 70% Q50 method should be used with caution in summer as this method provided flows in the range of 15–16% of MAF. Other methods provided environmental flows higher than 15% MAF, thus, providing better flow protection for aquatic habitat. When comparing water availability for off‐stream use (river flow–environmental flow), different parts of New Brunswick were found to be deficient in flows (i.e., river flows less than environment flows—no extractable water) during the summer and winter low‐flow periods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".