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Record W2768776297 · doi:10.3390/environments4040083

Vulnerability of Coastal Beach Tourism to Flooding: A Case Study of Galicia, Spain

2017· article· en· W2768776297 on OpenAlexaff
Diego Rodríguez-Toubes Muñiz, Stefan Gößling, C. Michael Hall, Daniel Scott

Bibliographic record

VenueEnvironments · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFlooding (psychology)Storm surgeTourismVulnerability (computing)Vulnerability indexGeographyCoastal floodFlood mythClimate changeWater resource managementEnvironmental resource managementStormEnvironmental planningEnvironmental scienceOceanographySea level riseGeology

Abstract

fetched live from OpenAlex

Flooding, as a result of heavy rains and/or storm surges, is a persistent problem in coastal areas. Under scenarios of climate change, there are expectations that flooding events will become more frequent in some areas and potentially more intense. This poses a potential threat to coastal communities relying heavily on coastal resources, such as beaches for tourism. This paper develops a methodology for the assessment of coastal flooding risks, based on an index that compares 16 hydrogeomorphological, biophysical, human exposure and resilience indicators, with a specific focus on tourism. The paper then uses an existing flood vulnerability assessment of 724 beaches in Galicia (Spain) to test the index for tourism. Results indicate that approximately 10% of tourism beaches are at high risk to flooding, including 10 urban and 36 rural beaches. Implications for adaptation and coastal management are discussed.

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)0.002
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 score0.123
Threshold uncertainty score0.245

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.293
Teacher spread0.271 · 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

Citations40
Published2017
Admission routes1
Has abstractyes

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