Applying baseflow approach to the environmental flow needs of the Similkameen River Watershed in British Columbia, Canada
Bibliographic record
Abstract
Study region The Similkameen River Watershed, an international basin, with a substantial portion located in British Columbia, Canada. It is a tributary to the Columbia River, which is the heavily regulated watershed in North America. Study focus Assessing Environmental Flow Needs (EFN) is imperative for preserving the socio-ecological balance and sustainability . The EFN BF method using baseflow as an index for EFN settings, addresses key challenges faced by conventional methods. In this study, we used discrete conductivity data in the upper, middle, and lower reaches of the watershed to estimate long-term daily conductivities from 1966 to 2017. These facilitated deriving the watershed-level baseflow index (BFI) using conductivity mass balance method, which was subsequently applied to calibrate Eckhardt's BFI max parameter. Using calibrated Eckhardt method, baseflow for 23 tributaries were derived allowing for quantification of EFN for spring, summer and fall-winter across tributaries by the EFN BF method. New hydrological insights for the region This study presents a reliable scheme for evaluating EFN based on a baseflow calculation using discrete conductivity data. The impacts of licensed water usage on the EFN in the watershed were examined in three tributaries, revealing that current licensed withdrawals exceed water availability during the fall-winter. This suggests more measures are needed to alleviate water stress and ensure the protection of EFN. Therefore, our established EFN can serve as the preliminary estimates of the Similkameen River Watershed for promoting river health and sustainable water management.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| 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".