Biogeochemical effects of Golden Gate Strait exchange and other land-base inputs to the San Francisco and Monterey Bay coasts
Bibliographic record
Abstract
The central California coast between San Francisco Bay (SFB) and Monterey Bay (MB) is an upwelling-dominated marine ecosystem with a coastal population of 8.5 million. Anthropogenically enhanced terrestrial nutrients enter the ocean via three primary pathways: (1) SFB exchanges across the Golden Gate Strait, (2) coastal rivers, and (3) municipal wastewater discharged to ocean outfalls. The consequences of these inputs on primary production, acidification and hypoxia remain poorly understood. Here, we investigate these effects with a submesoscale-resolving ocean biogeochemical model. Simulations show that while terrestrial nutrient inputs collectively affect a broad region, the stronger impacts are found in nearshore waters, increasing dissolved inorganic nitrogen by 11.4%, primary production by 6.5%, and chlorophyll concentration by 4.5% along a 15-km coastal band. While exchanges from the SFB dominate these effects, all sources, including coastal rivers and ocean outfalls, produce distinct, localized footprints. Subsurface oxygen and pH decline due to terrestrial nutrient loading, but vigorous upwelling and circulation limit the intensity of these changes. Enhanced nutrient inputs are predicted to promote conditions favorable for diatom growth, potentially including Pseudo-nitzschia spp., creating an environment more conducive to domoic acid (DA) production. Model results show that chlorophyll concentrations exceed the threshold associated with elevated DA risk on 10-45% more days under nutrient-enriched conditions, compared to a scenario without terrestrial inputs. These findings highlight the need for expanded observational and modeling efforts to better understand the ecological consequences of anthropogenic nutrient inputs along the central California coast.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".