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Record W4236877627 · doi:10.1061/9780784480595.056

Water Allocation Model in the Lancing-Mekong River Basin Based on Bankruptcy Theory and Bargaining Game

2017· article· en· W4236877627 on OpenAlexaff
Liang Yuan, Weijun He, Dagmawi Mulugeta Degefu, Zaiyi Liao, Xia Wu

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMekong riverWater resourcesDrainage basinWater resource managementAllocative efficiencyMainland ChinaStructural basinChinaBankruptcySustainable developmentGame theoryEnvironmental scienceBusinessGeographyEconomicsGeologyFinancePolitical science

Abstract

fetched live from OpenAlex

The Mekong River is the dominant geo-hydrological structure in mainland Southeast Asia, originating in China and flowing through or bordering Myanmar, Laos, Thailand, Cambodia and Vietnam. With increasing economic development and raising water demand the possibility of water conflict could increase in transboundary river basins. This is the case in the Lancing-Mekong River Basin in recent years, especially during the dry season. More and more scholars are paying attention to build stable, equitable, and environmentally sustainable water allocation method that supports the socio-economic development in transboundary river basins. In this research the authors use the bankruptcy and game theory to construct a new cooperation bankruptcy bargaining game model to allocate water resources. The results of this study could provide ways which could help water allocation and avoid water conflicts in transboundary river basins.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.009
GPT teacher head0.187
Teacher spread0.178 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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