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Record W4225322346 · doi:10.5194/hess-2022-172-rc1

Comment on hess-2022-172

2022· peer-review· en· W4225322346 on OpenAlexaff
Mohammad Ghoreishi, Amin Elshorbagy, Saman Razavi, Gà ⁄ nter Blöschl, Murugesu Sivapalan, Ahmed Abdelkader

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
FundersYunnan University
KeywordsRiparian zoneStructural basinPoliticsDrainage basinGeographyWork (physics)Political scienceEnvironmental resource managementEconomicsCartographyEcologyEngineeringGeology

Abstract

fetched live from OpenAlex

While conflict-and-cooperation phenomena in transboundary basins have been widely studied, much less work has been devoted to representing the process interactions in a quantitative way. This paper identifies the main factors in the riparian countries’ willingness to cooperate in the Eastern Nile River Basin, involving Ethiopia, Sudan, and Egypt, from 1983 to 2016. We propose a quantitative model of the willingness to cooperate at the national and river basin scales. Our results suggest that relative political stability and foreign direct investment can explain Ethiopia’s decreasing willingness to cooperate between 2009 and 2016. Further, we show that the 2008 food crisis may account for Sudan recovering its willingness to cooperate with Ethiopia. Long-term lack of trust among the riparian countries may have reduced basin-wide cooperation. While the proposed model has some limitations regarding model assumptions and parameters, it does provide a quantitative representation of the evolution of cooperation pathways among the riparian countries, which can be used to explore the effects of changes in future dam operation and other management decisions on the emergence of conflict and cooperation in the basin.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.320
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1170.001

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 head0.056
GPT teacher head0.349
Teacher spread0.293 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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