The contribution of glacial isostatic adjustment to projections of sea‐level change along the Atlantic and Gulf coasts of North America
Bibliographic record
Abstract
Abstract We determine the contribution of glacial isostatic adjustment (GIA) to future relative sea‐level change for the North American coastline between Newfoundland and Texas. We infer GIA model parameters using recently compiled and quality‐assessed databases of past sea‐level changes, including new databases for the United States Gulf Coast and Atlantic Canada. At 13 cities along this coastline, we estimate the GIA contribution to range from a few centimeters (e.g., 3 [−1 to 9] cm Miami) to a few decimeters (e.g., 18 [12–22] cm, Halifax) for the period 2085–2100 relative to 2006–2015 (1−σ ranges given). We provide estimates of uncertainty in the GIA component using two different methods; the more conservative approach produces total ranges (1−σ confidence) that vary from 3 to 16 cm for the cities considered. Contributions from ocean steric and dynamic changes as well as those from changes in land ice are also estimated to provide context for the GIA projections. When summing the contributions from all three processes at the 13 cities considered along this coastline, using median or best‐estimate values, the GIA signal comprises 5–38% of the total depending on the adopted climate forcing and location. The contributions from ocean dynamic/steric changes and ice mass loss are similar in amplitude but with spatial variation that approximately cancels, resulting in GIA dominating the net spatial variability north of 35°N.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".