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Record W4401666666 · doi:10.1038/s41467-024-51456-w

Can continental transboundary compacts hold water?

2024· article· en· W4401666666 on OpenAlexfundaboutno aff
Andrew D. Gronewold, Jenna Bednar, Marjorie Cort, Vianey Rueda, Michael R. Moore, Jon Allan

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsnot available
FundersU.S. Army Corps of EngineersCommission for Environmental CooperationColorado State UniversityU.S. Geological SurveyBill and Melinda Gates FoundationNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsTreatyAridPoliticsEnvironmental resource managementEnvironmental planningEnvironmental protectionGeographyBusinessPolitical scienceEnvironmental scienceLawEcology

Abstract

fetched live from OpenAlex

The growing number of threats facing continental-scale transboundary water treaties warrants contemporary evaluation of not only the political and climatological conditions under which they were constructed, but also of how different management strategies for accommodating changes in those conditions can lead to treaty success or failure. We assess these threats by highlighting key attributes and vulnerabilities of water treaties across North America that frame a diverse set of future water management priorities. While these threats are ubiquitous globally, they are particularly pronounced in North America where water-abundant basins along the border between the United States (US) and Canada contrast with arid basins along the border between the US and Mexico. We propose addressing these needs through a three-step call to action for management agencies, politicians, and the public at large to embrace a holistic perspective on transboundary water agreements.

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.013
Scholarly communication0.0140.023
Open science0.0010.010
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0290.001

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 head0.027
GPT teacher head0.327
Teacher spread0.300 · 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 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

Citations8
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueNature CommunicationsSame topicTransboundary Water Resource ManagementFrench-language works237,207