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Record W2808783827 · doi:10.1061/9780784480304.061

Selection of Appropriate Coastal Protection Strategies for Caribbean Coastlines

2017· article· en· W2808783827 on OpenAlexaff
Jamel D. Banton, Philip Warner, David A. Y. Smith, Véronique Morin

Bibliographic record

VenueCoastal Structures and Solutions to Coastal Disasters 2015 · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsShoreCaribbean regionStakeholderEnforcementEnvironmental resource managementEnvironmental planningCoastal erosionProcess (computing)Coastal managementGeographyEnvironmental protectionBusinessOceanographyComputer sciencePolitical scienceEnvironmental scienceLatin Americans

Abstract

fetched live from OpenAlex

The coastlines of the Caribbean are unique in many ways. Their warm waters and sandy beaches attract millions of visitors to their shores and these attributes provide an essential economic opportunity. Coastal hazards affecting the region include hurricanes, tsunami, and coastal erosion. For islands with mountainous interiors, development is often concentrated along the coastlines. This paper draws on more than 20 years of experience designing and evaluating coastal protection schemes in the region, and the related issues, constraints and opportunities are discussed. Projects in the Bahamas, St. Vincent, Guyana, Jamaica, and Barbados are cited and the reasons for their appropriateness to the region and specific locations are highlighted. The paper highlights the importance of multi-criteria analyses for coastal project selection and the need for funding agencies to insist on a thorough evaluation of options at the design stage. The unique nature of both the local and regional issues must be recognized and, as such, the design parameters used must have a Caribbean focus. Stakeholder consultation is an essential component of the design process and projects should be treated as an opportunity for socio-economic enhancement as much as possible. Eco-system based approaches should also be given due consideration. Experience has shown that the regulations and codes that govern coastal development need to be updated in most cases. Further, proper enforcement of these regulations will be key if Caribbean coastlines are to survive the next century of anticipated 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.262
Teacher spread0.237 · 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 designOther design
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

Citations2
Published2017
Admission routes1
Has abstractyes

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