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Sustainability Risks of Coastal Cities from Climate Change

2017· article· en· W2735879862 on OpenAlexaff
Edward A. McBean, Jinhui Jeanne Huang‬‬‬‬

Bibliographic record

VenueThe Global Environmental Engineers · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSustainabilityStorm surgeClimate changeContext (archaeology)Sea level riseGeographyPopulationPopulation growthStormGlobal warmingSea levelEnvironmental sciencePhysical geographyOceanographyGeologyMeteorologyEcology

Abstract

fetched live from OpenAlex

Issues influencing the sustainability of coastal cities are assessed, reflecting the combination of impending sea level rise and storm surges, and increasing growth in populations in coastal cities. Geologic-time scales are utilized to draw parallels to characterize relevant historical occurrences that help to understand the context of projections of impending sea level rise issue to year 2100. Given that Antarctica holds sufficient water to raise global sea levels by 58 m if the ice were to melt, this indicates that even a small percentage of melting of the polar ice caps, should this occur, will have enormous implications to the sustainability of coastal cities which are projected to hold 12.4 percent of the world’s population by 2060. The result is the combination of predicted sea level rise and associated storm surges indicate that drastic measures must be promoted to improve the sustainability of coastal cities.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.236
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2017
Admission routes1
Has abstractyes

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