Sensitivity of the regional ocean acidification and carbonate system in Puget Sound to ocean and freshwater inputs
Bibliographic record
Abstract
While ocean acidification was first investigated as a global phenomenon, coastal acidification has received significant attention in recent years, as its impacts have been felt by different socio-economic sectors (e.g., high mortality of shellfish larvae in aquaculture farms). As a region that connects land and ocean, the Salish Sea (consisting of Puget Sound and the Straits of Juan de Fuca and Georgia) receives inputs from many different sources (rivers, wastewater treatment plants, industrial waste treatment facilities, etc.), making these coastal waters vulnerable to acidification. Moreover, the lowering of pH in the Northeast Pacific Ocean also affects the Salish Sea, as more acidic waters get transported into the bottom waters of the straits and estuaries. Here, we use a numerical ocean model of the Salish Sea to improve our understanding of the carbonate system in Puget Sound; in particular, we studied the sensitivity of carbonate variables (e.g., dissolved inorganic carbon, total alkalinity, pH, saturation state of aragonite) to ocean and freshwater inputs. The model is an updated version of our FVCOM-ICM framework, with new carbonate-system and sediment modules. Sensitivity experiments altering concentrations at the open boundaries and freshwater sources indicate that not only ocean conditions entering the Strait of Juan de Fuca, but also the dilution of carbonate variables by freshwater sources, are key drivers of the carbonate system in Puget Sound.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".