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Record W2133097658 · doi:10.1061/9780784412688.011

Carbon Dioxide Emissions by the Transportation Sector in Kathmandu Valley, Nepal

2012· article· en· W2133097658 on OpenAlexaboutno aff
Pramen P. Shrestha, K. Joseph Shrestha, Krishna Prasad Shrestha

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationEnvironmental scienceCarbon dioxideAir pollutionAir quality indexPopulation densityPollutionEnvironmental engineeringGeographyEnvironmental protectionMeteorologyEnvironmental health

Abstract

fetched live from OpenAlex

Kathmandu Valley (approximately 570 km2) is the largest city and the capital of Nepal. In 1991, it had a population of 1.1 million. The population rose to nearly 2.5 million in 2011, with a population density of 4,386/km². This is about 1.6 times the population density of an average Canadian urbanized area, which is 2,656/km2. The transportation sector is the largest source of air pollution in the valley. In 1990, there were 40,133 vehicles in the valley. This number reached 330,336 in 2011, an increase of 8-fold within 20 years. Recent data showed that there are 15,008 diesel and 315,328 gasoline vehicles. This paper determines the amount of carbon dioxide (CO2) emitted by these vehicles. Vehicle data were collected from the Department of Motor Vehicles, Kathmandu, Nepal. The emission rate of each type of vehicle was collected from the vehicle manufacturers. The study showed that Kathmandu Valley had highest level of pollution in Nepal. The CO2 emission from each type of vehicle also was calculated. Recommendations regarding improving the air quality of the valley are presented.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.233

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 communication0.0000.000
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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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