U.S. Pacific coastal wetland resilience and vulnerability to sea-level rise
Bibliographic record
Abstract
We used a first-of-its-kind comprehensive scenario approach to evaluate both the vertical and horizontal response of tidal wetlands to projected changes in the rate of sea-level rise (SLR) across 14 estuaries along the Pacific coast of the continental United States. Throughout the U.S. Pacific region, we found that tidal wetlands are highly vulnerable to end-of-century submergence, with resulting extensive loss of habitat. Using higher-range SLR scenarios, all high and middle marsh habitats were lost, with 83% of current tidal wetlands transitioning to unvegetated habitats by 2110. The wetland area lost was greater in California and Oregon (100%) but still severe in Washington, with 68% submerged by the end of the century. The only wetland habitat remaining at the end of the century was low marsh under higher-range SLR rates. Tidal wetland loss was also likely under more conservative SLR scenarios, including loss of 95% of high marsh and 60% of middle marsh habitats by the end of the century. Horizontal migration of most wetlands was constrained by coastal development or steep topography, with just two wetland sites having sufficient upland space for migration and the possibility for nearly 1:1 replacement, making SLR threats particularly high in this region and generally undocumented. With low vertical accretion rates and little upland migration space, Pacific coast tidal wetlands are at imminent risk of submergence with projected rates of rapid SLR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".