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Record W3184787450 · doi:10.47205/jdss.2021(2-ii)03

Cost-Benefit Analysis and Technical Viability of Kalabagh Dam

2021· article· en· W3184787450 on OpenAlexaff
Qasim Shahzad Gill

Bibliographic record

VenueJournal of Development and Social Sciences · 2021
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCost analysisRisk analysis (engineering)Environmental scienceBusinessEngineeringReliability engineering

Abstract

fetched live from OpenAlex

This article spells out the details of Kalabagh Dam project such as location and technical specifications of the project as well as the costbenefit analysis of the dam.In addition, this article accounts for the technical apprehensions regarding Kalabagh Dam.The dam was also aimed at augmenting agricultural growth, providing a cushion against flood threats, generating low-priced hydro-power and lastly to compensate for the depleting capacity of the existing mega reservoirs to store water due to sedimentation.The theory deployed on the article known as cost-benefit analysis delineates the worth of the mega project of Pakistan but it is hard to say that it is still unfixed due to decision making approach of politicians of the country who remained unable to resolve it.Incumbent government must take Kalabagh Dam under consideration to build it as soon as possible because of its natural site and to overcome the energy slowdown.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.022
GPT teacher head0.248
Teacher spread0.226 · 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 designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

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