Bibliographic record
Abstract
Sea level rise due to climate warming will amplify coastal hazards such as storm surges, beach erosion, and loss of wetlands. Sea level in the New York metropolitan region has risen steadily by 22–39 cm during the 20th century. Projections based on both historic trends and climate model simulations (Hadley Centre, UK and Canadian Centre for Climate Modelling and Analysis) suggest that regional sea levels could climb another 18–60 cm by the 2050s and 24–108 cm by the 2080s, over late 20th century levels. The return period of the 100-year storm flood could be reduced, on average, to 19–68 years by the 2050s and 4–60 years by the 2080s, resulting in more frequent damaging coastal floods. Around 38% of the land surface of salt marsh islands has disappeared in Jamaica Bay, New York City between 1974 and 1999, due to the interaction of a number of anthropogenic factors and sea level rise. Given these stresses, the saltmarshes are not likely to survive accelerated sea level rise without urgent remedial action. The modest sea level change projected for the next 20–30 years provides a grace period during which coastal managers, planners, and other stakeholders can develop appropriate mitigation/adaptation strategies and policies to cope with longer-term sea level rise.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".