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Record W2316086516 · doi:10.1061/40605(258)37

Sea Level Rise in the New York CIty Metropolitan Area

2002· article· en· W2316086516 on OpenAlexaboutno aff
Vivien Gornitz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsnot available
FundersUniversity of Illinois at Urbana-ChampaignU.S. Army Corps of EngineersNew York State Department of Environmental ConservationNational Science Foundation
KeywordsStorm surgeSea levelBayMetropolitan areaSea level riseStormCoastal floodFlood mythClimate changeCoastal erosionEnvironmental scienceTide gaugeGeographyOceanographyPhysical geographyShoreGeologyMeteorologyArchaeology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.650
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.000

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.

Opus teacher head0.061
GPT teacher head0.215
Teacher spread0.154 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2002
Admission routes1
Has abstractyes

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