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Record W2107960291 · doi:10.1038/ncomms4794

The effectiveness of coral reefs for coastal hazard risk reduction and adaptation

2014· article· en· W2107960291 on OpenAlexaff
Filippo Ferrario, Michael W. Beck, Curt D. Storlazzi, Fiorenza Micheli, Christine C. Shepard, Laura Airoldi

Bibliographic record

VenueNature Communications · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversité Laval
FundersU.S. Geological Survey
KeywordsReefCoral reefResilience of coral reefsCoral reef protectionFlooding (psychology)Coral reef organizationsFisheryEnvironmental scienceCoastal hazardsClimate changeEcologyOceanographySea level riseGeologyBiology

Abstract

fetched live from OpenAlex

The world's coastal zones are experiencing rapid development and an increase in storms and flooding. These hazards put coastal communities at heightened risk, which may increase with habitat loss. Here we analyse globally the role and cost effectiveness of coral reefs in risk reduction. Meta-analyses reveal that coral reefs provide substantial protection against natural hazards by reducing wave energy by an average of 97%. Reef crests alone dissipate most of this energy (86%). There are 100 million or more people who may receive risk reduction benefits from reefs or bear hazard mitigation and adaptation costs if reefs are degraded. We show that coral reefs can provide comparable wave attenuation benefits to artificial defences such as breakwaters, and reef defences can be enhanced cost effectively. Reefs face growing threats yet there is opportunity to guide adaptation and hazard mitigation investments towards reef restoration to strengthen this first line of coastal defence.

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)0.008
metaresearch head score (Gemma)0.017
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 score0.009
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.014
GPT teacher head0.257
Teacher spread0.243 · 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

Citations969
Published2014
Admission routes1
Has abstractyes

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