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Record W2623249120 · doi:10.5539/eer.v7n1p48

Post - Tsunami Damages Assessment, Relief and Rehabilitation Measures in the Kanyakumari District, Tamil Nadu, South India

2017· article· en· W2623249120 on OpenAlexvenueno aff
C. Hentry, S. Saravanan, N. Chandrasekar, S. L. Rayar

Bibliographic record

VenueEnergy and Environment Research · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsnot available
FundersUniversity Grants Commission
KeywordsSeawallShoreCoastal erosionTamilDamagesProsperityBayGeographySocioeconomicsOceanographyFisheryGeology

Abstract

fetched live from OpenAlex

The present study is based on the post tsunami survey conducted in January 2005 along the south west coast of India. This paper illustrates the variation of tsunami intensity along the coasts of Kanyakumari district and the consequent morphological changes occurred in the coastal area during tsunami. 33 coastal habitations of Kanyakumari district faced the wrath of tsunami waves. We have attempted to study shoreline modifications using beach profile survey, tsunami sand deposits, inundation distance, run up elevation and tsunami height. The coastal environment changes through online survey on human losses, housing and shelter, fisheries, agriculture and damages of infrastructure were studied in the study area. The major destructions identified in this area were 3m-4m sea water rise leading to erosion activities and changes in the beach slope variation. Many gentle slope regions have been transformed into steeply sloped regions. This unknown killer wave causes causalities and mass destruction in the coastal environment due to unaware of tsunami. The shallow waters of bays and estuaries have initiated the seizing. Maximum wave activity observed in the tsunami area closely to that of the bay, low lying areas etc. Initial relief by way of food, clothing, shelter and first-aid medical help has reached most of the affected communities by NGO’s and Government agencies were discussed. The paper also explained the innovative comprehensive long-term rehabilitation program aimed to go beyond basic restoration and rather upgrade standard of living to ensure sustained well-being and prosperity of the tsunami affected people.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.105
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.025
GPT teacher head0.281
Teacher spread0.256 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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