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Record W2912239779 · doi:10.3390/ijerph16030450

The Impact of Upstream Sub-Basins’ Water Use on Middle Stream and Downstream Sub-Basins’ Water Security at Country-Basin Unit Spatial Scale and Monthly Temporal Resolution

2019· article· en· W2912239779 on OpenAlexaff
Dagmawi Mulugeta Degefu, Zaiyi Liao, Weijun He, Liang Yuan, Min An, Zhaofang Zhang, Xia Wu

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2019
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsToronto Metropolitan University
FundersNational Natural Science Foundation of China
KeywordsStructural basinUpstream (networking)Riparian zoneDownstream (manufacturing)Drainage basinWater securityHydrology (agriculture)Environmental scienceWater resourcesGeographyGeologyBusinessGeomorphologyEcologyHabitat

Abstract

fetched live from OpenAlex

Water, in most of the transboundary river basins, is a bone of contention among their riparian states. Taking this into account, this article assessed the monthly impact of upstream water withdrawal on the water security of middle stream and downstream sub-basins at a country-basin mesh spatial resolution. Roughly 2.18 billion people in 442 sub-basin areas experience water stress intensification by less than 1% throughout the year. In addition, 2.12 billion people in 336 sub-basin areas experience water stress level change, from no water stress to one of the water stress categories, for at least one month as the result of upstream withdrawal. Even though there is a clear upstream impact in many of the basins, water disputes with severe social, economic, political, and environmental consequences are nonexistent. This might be an indication that grave water disputes are the result of complex socio-economic and political interactions, not merely because of water deficits due to upstream water withdrawal. Therefore, understanding this relationship is crucial in identifying inflection points for water conflicts within 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 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)0.001
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 score0.092
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.027
GPT teacher head0.268
Teacher spread0.241 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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