MétaCan
Menu
Back to cohort
Record W2093500183 · doi:10.1108/09653560911003679

Natural hazards and environmental implications in Nepal

2009· article· en· W2093500183 on OpenAlexaff
D. Pokhrel, B.S. Bhandari, T. Viraraghavan

Bibliographic record

VenueDisaster Prevention and Management An International Journal · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversity of CalgaryUniversity of Regina
Fundersnot available
KeywordsFlash floodLandslideNatural disasterNatural hazardFlood mythGeographySanitationNatural (archaeology)Environmental scienceEnvironmental planningWater resource managementEnvironmental engineeringEngineeringMeteorology

Abstract

fetched live from OpenAlex

Purpose The purpose of the paper is to evaluate the published information on natural hazards and their implications in the environment. Design/methodology/approach The published data/information on natural hazards were collected and analyzed. Findings An analysis of the disaster data (1983‐2003) showed that a total of 1,063 lives on an average were claimed by natural calamities (earthquake, landslide, flood, fire, windstorm, epidemics and avalanche) each year. Water‐induced (flood and landslide) disasters alone contributed to 31.8 percent of the total deaths. Epidemics claimed the maximum number of deaths (55.9 percent) especially in the post‐disaster period. Many of these epidemics occurred due to the contamination of the drinking water sources by flash floods, and landslides. Poor sanitation, unsafe water and unhealthy living conditions contributed to major outbreaks of water‐borne diseases especially in the monsoon period claiming numerous lives. Originality/value Published information on natural hazards and implications on environment is limited. This paper integrated and analyzed 21 years of disaster data. A discussion of environmental implication is provided.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.376

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.322
Teacher spread0.311 · 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

Citations15
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueDisaster Prevention and Management An International JournalSame topicDisaster Management and ResilienceFrench-language works237,207