The Impact of a Modest Anthropogenic Carbon Increase on the Carbonate Chemistry Balance of a Temperate Fjord System
Bibliographic record
Abstract
Coastal regions are typically characterized by considerable physical variability that in turn leads to dramatic variability in coastal carbonate chemistry. Recent shipboard and mooring-based observations have shown large spatial and temporal variations of carbonate chemistry parameters, including air-sea CO2 flux and aragonite saturation state, in one prominent coastal region in the Northeast Pacific Ocean - the Salish Sea. The range of the observed variability in the regional carbonate system is significantly larger than the global anthropogenic change, complicating the detection of secular carbon trends. Simultaneously, sparse observations limit understanding of the carbonate balance as a whole. Here, we use a highly resolved coastal model, SalishSeaCast, to characterize the drivers of the carbonate chemistry balance of the Salish Sea, with an emphasis on air-sea CO2 flux and aragonite saturation state. We then investigate the impact of a relatively modest increase in anthropogenic carbon in this region in the context of the governing physical and biological dynamics of the system. We examine the striking effects of the anthropogenic change to date on the inorganic carbon balance of the system, highlighting impacts on the aragonite saturation state of the system and its buffering capacity, as well as suggesting some bounds for the regional air-sea and lateral carbon fluxes. We then use the GLODAP dataset of global coastal carbon observations to consider our results in the context of other regions of the Pacific Rim and the global coastal ocean.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".