Spatial variability of late Holocene and 20th century sea-level rise along the Atlantic coast of the United States
Bibliographic record
Abstract
Accurate estimates of global sea-level rise in the pre-satellite era provide a context for 21stcentury sea-level predictions, but the use of tide-gauge records is complicated by the contributions from changes in land level due to glacial isostatic adjustment (GIA). We have constructed a rigorous quality-controlled database of late Holocene sea-level indices from the U.S. Atlantic coast, exhibiting subsidence rates of <0.8 mm a–1 in Maine, increasing to rates of 1.7 mm a–1 in Delaware, and a return to rates <0.9 mm a–1 in the Carolinas. This pattern can be attributed to ongoing GIA due to the demise of the Laurentide Ice Sheet. Our data allow us to define the geometry of the associated collapsing proglacial forebulge with a level of resolution unmatched by any other currently available method. The corresponding rates of relative sea-level rise serve as background rates on which future sea-level rise must be superimposed. We further employ the geological data to remove the GIA component from tide-gauge records to estimate a mean 20th century sea-level rise rate for the U.S. Atlantic coast of 1.8 ± 0.2 mm a–1, similar to the global average. However, we find a distinct spatial trend in the rate of 20th century sea-level rise, increasing from Maine to South Carolina. This is the first evidence of this phenomenon from observational data alone. We suggest this may be related to the melting of the Greenland ice sheet and/or ocean steric effects.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".