The sensitivity of estuarine aragonite saturation state and pH to the carbonate chemistry of a freshet-dominated river
Bibliographic record
Abstract
Abstract. Ocean acidification threatens to reduce pH and aragonite saturation state (ΩA) in estuaries, potentially damaging their ecosystems. However, the impact of highly variable river total alkalinity (TA) and dissolved inorganic carbon (DIC) on pH and ΩA in these estuaries is unknown. We assess the sensitivity of estuarine surface pH and ΩA to river chemistry using a 1-dimensional, biogeochemical-coupled model of the Strait of Georgia on the Canadian Pacific coast and generalize the results in the context of global rivers. The productive Strait of Georgia estuary has a large, seasonally variable freshwater input from the glacially fed, undammed Fraser River. Analyzing TA and pH observations from this river and its estuary, we find that the Fraser is moderately alkaline (TA 500–1350 μmol kg−1) but relatively DIC-rich, especially during winter (low flow). Model results show that estuarine pH and ΩA, while sensitive to freshwater DIC and TA, do not vary in synchrony. Instead, rivers with high DIC and TA produce lower estuarine pH due to an increased estuarine DIC : TA ratio, but higher estuarine ΩA because of DIC contributions to the carbonate ion. This estuarine pH sensitivity decreases with increasing mean river TA, but the zone of maximum pH sensitivity also moves to higher salinity which could impact a larger areal extent of the estuary. Many temperate rivers, such as the Fraser, are expected to experience weaker freshets and stronger winter flows under climate change, reducing the extent of the river plume and the impact of river chemistry in much of the estuary. However, increasing carbon in rivers will move the highest sensitivity zone to higher salinities that cover larger areas under present-day flow regimes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".