Initial sensitivity analysis of nutrient loading to understand hypoxia in the Salish Sea sub-basins
Bibliographic record
Abstract
The Salih Sea is a complex estuarine system crossing over the U.S and Canadian waters. It has high cultural, environmental, and economic importance, but the increasing levels of nutrient pollution have threatened its values and caused hypoxia, algal blooms, and fish kills. To understand the impact of nutrient loading on the Salish Sea, we used a diagnostic hydrodynamic and biogeochemical model of the Salish Sea (Salish Sea Model) which has been developed to simulate circulation and biogeochemical cycling. For a deeper understanding of the interaction between land-based nutrient loading and circulation characteristic of the Salish Sea, we conducted initial components of a sensitivity analysis of different nutrient loading scenarios, with the nutrient reduction from non-point source loads to those from marine wastewater outfalls. We focused the analysis on the sub-basins in the Salish Sea such as Hood Canal Basin, Admiralty Inlet, Bellingham Bay, Whidbey Basin, Central Basin, and South Sound. We found that sub-basins respond differently to scenarios, likely driven by underlying differences in their physical and biogeochemical characteristics. To consider both temporal and spatial Dissolved Oxygen variation, we applied a novel analysis of calculated Dissolved Oxygen, including calculations of cumulative noncompliance-volume-days and hypoxic-volume-days. The cumulative volume days calculation is an extension of the method of marine water quality criteria of State of Washington, to provide further detail within the water column. The hypoxic volume days calculation is based on classic representations of hypoxia volume (dissolved oxygen < 2 mg/L).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".