Flow Restoration on Salish Sea Rivers
Bibliographic record
Abstract
The Salish Sea receives freshwater, nutrients, and anthropogenic inputs from more than ten rivers and many sub-watersheds. The Fraser River alone contributes as much as 46% of the freshwater discharge, while 24% of discharge is from Puget Sound Rivers to the Salish Sea. These flows have a direct role on the Salish Sea ecosystem, impacting water quality and influencing Salmonid and other aquatic species habitat. A vital component of a healthy Salish Sea ecosystem now and in the future is the quantity of flow in these rivers and streams. Climate change makes flow restoration even more urgent in Salish Sea watersheds. The purpose of this session is to engage in a conversation on the role of flow on a healthy Salish Sea and the critical need to improve watershed function and flow, especially with the impacts of climate change. The session will provide an overview of flow restoration tools and techniques being utilized and developed in Salish Sea watersheds with case studies/project examples by multiple presenters from Canada and US. Discussion may include efforts such as: Dungeness River: Using water banking, water rights transactions, and large infrastructure projects to protect flow at fish critical periods (a new off-channel reservoir for up to 50% flow improvement), sustainably allocate new uses, and create resilient water resource management systems. Nooksack River: A Payment for Watershed Services (PWS) model and adaptive timber harvest rotations on private lands to increase late season flows in the face of climate change. Sammamish River: Providing highly treated recycled water for irrigation, switching users from surface and shallow groundwater to restore as much as 5 cfs to the river and reduce Salish Sea nutrient loading. Flow restoration efforts will be shared from a diverse array of presenters throughout the Salish Sea region to further best practices and foster collaboration.
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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 | 0.001 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".