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Record W1979831804 · doi:10.1193/1.2201971

Coastal Ecosystems and Tsunami Protection after the December 2004 Indian Ocean Tsunami

2006· article· en· W1979831804 on OpenAlexafffund
Stephanie E. Chang, Beverley J. Adams, Jacqueline Alder, Philip Berke, Ratana Chuenpagdee, Shubharoop Ghosh, Colette C. C. Wabnitz

Bibliographic record

VenueEarthquake Spectra · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsDalhousie UniversityVancouver Community CollegeUniversity of British Columbia
FundersUniversity of British ColumbiaEngineering Research CentersOregon State UniversityNational Science Foundation
KeywordsMangroveGeographyMangrove ecosystemCapital cityEcosystemEnvironmental resource managementEnvironmental protectionEnvironmental planningFisheryEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

An exploratory study was conducted on the role of coastal ecosystems in protecting communities from the December 2004 Indian Ocean tsunami, focusing on mangrove forests on the Andaman coast of Thailand and how well villages were undertaking environmental conservation. Remote sensing analysis identified predisaster mangrove change and postdisaster structural damage and landscape changes. Field data from five sites (20 villages), gathered via the VIEWS™ data collection system, validated and supplemented this analysis. Key informants at several of these villages were also interviewed. A preliminary comparison of villages that otherwise faced similar tsunami exposure suggests that the presence of healthy mangroves did afford substantial protection. Village performance in mangrove conservation and management efforts, and thus the presence of healthy forests, is influenced by both social capital and the design of external aid delivery programs.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.003
GPT teacher head0.168
Teacher spread0.165 · 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

Citations74
Published2006
Admission routes2
Has abstractyes

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