Natural and Anthropogenic Drivers of Acidification in Large Estuaries
Bibliographic record
Abstract
Oceanic uptake of anthropogenic carbon dioxide (CO2) from the atmosphere has changed ocean biogeochemistry and threatened the health of organisms through a process known as ocean acidification (OA). Such large-scale changes affect ecosystem functions and can have impacts on societal uses, fisheries resources, and economies. In many large estuaries, anthropogenic CO2-induced acidification is enhanced by strong stratification, long water residence times, eutrophication, and a weak acid–base buffer capacity. In this article, we review how a variety of processes influence aquatic acid–base properties in estuarine waters, including coastal upwelling, river–ocean mixing, air–water gas exchange, biological production and subsequent aerobic and anaerobic respiration, calcium carbonate (CaCO3) dissolution, and benthic inputs. We emphasize the spatial and temporal dynamics of partial pressure of CO2 ( pCO2), pH, and calcium carbonate mineral saturation states. Examples from three large estuaries—Chesapeake Bay, the Salish Sea, and Prince William Sound—are used to illustrate how natural and anthropogenic processes and climate change may manifest differently across estuaries, as well as the biological implications of OA on coastal calcifiers.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".