Bibliographic record
Abstract
Groundwater–surface water exchange in salt marsh ecosystems mediates nearshore salt, nutrient, and carbon budgets with implications for biological productivity and global climate. Despite their importance, a synthesis of salt marsh groundwater studies is lacking. In this review, we summarize drivers mediating salt marsh hydrogeology, review field and modeling techniques, and discuss patterns of exchange. New data from a Delaware seepage meter study are reported which highlight small-scale spatial variability in exchange rates. A synthesis of the salt marsh hydrogeology literature reveals a positive relationship between tidal range and submarine groundwater discharge but not porewater exchange, highlighting the multidimensional drivers of marsh hydrogeology. Field studies are heavily biased towards microtidal systems of the US East Coast, with little global information available. A preliminary estimate of marsh porewater exchange along the Mid-Atlantic and South Atlantic Bights is 8–30 × 1013 L y−1, equivalent to recirculating the entire volume of seawater overlying the shelf through tidal marsh sediments in ~30–90 years. This review concludes with a discussion of critical questions to address that will decrease uncertainty in global budget estimates and enhance our capacity to predict future responses to global climate change.
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 | 0.003 | 0.004 |
| 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) | 0.005 | 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".