Modeling freshwater from a subset of rivers throughout the Salish Sea using dye tracers
Bibliographic record
Abstract
The Salish Sea is a biologically productive coastal sea that is home to a human population of over 8.9 million residents shared between the USA and Canada, and supports high but threatened ecosystem diversity and species richness. It is an estuarine system with freshwater inputs from numerous rivers that influence ocean dynamics, biogeochemistry, and ecosystem processes. While previous studies have examined circulation and salinity patterns of freshwater, primarily focusing on the Fraser River in the Strait of Georgia or Puget Rivers in Puget Sound, the relative influence of different riverine sources on basins other than the one they directly feed into is unclear. This study first evaluates the performance of the SalishSeaCast model at capturing salinity patterns in small river plume regions for 11 key rivers using two different model versions, finding that the newer version with daily estimates of river flow improves the model's performance. Then, passive model dye tracers are used to examine the distribution of freshwater from river inputs in the Salish Sea from those 11 rivers using the new model version, with emphasis on examining inter-basin transport between the Strait of Georgia and Puget Sound. The study utilizes a physics-only version of SalishSeaCast, a 3-D ocean model built on the NEMO framework with half-kilometer horizontal grid resolution. Model results indicate that while the Fraser River has the largest influence on riverine freshwater compared to any other individual river, smaller rivers are non-negligible in relative magnitude and their influences are present throughout the Salish Sea. The findings suggest that smaller rivers play a role in salinity distributions and freshwater content throughout the Salish Sea, highlighting the need to consider their contributions in model development and analyses across Salish Sea basins. By advancing the understanding of freshwater dynamics in the Salish Sea, this study provides insights for future research on estuarine circulation, ecosystem impacts, and climate change resilience.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".