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Record W2139068826 · doi:10.1017/s1355770x07003701

Sharing the load? Floods, droughts, and managing international rivers

2007· article· en· W2139068826 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironment and Development Economics · 2007
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of British Columbia, Okanagan CampusOkanagan Science Centre
Fundersnot available
KeywordsStylized factDownstream (manufacturing)Upstream (networking)AridScope (computer science)Flooding (psychology)BusinessEnvironmental resource managementEnvironmental scienceEnforcementNatural resource economicsEnvironmental planningWater resource managementComputer scienceEconomicsEcology

Abstract

fetched live from OpenAlex

Rivers can be both givers of life and takers of life. Investments that provide protection against flooding are often beneficial during normal or low flows. Investments such as storage reservoirs are long lived, separating construction and management operations. With international rivers, the absence of enforcement mechanisms may preclude infrastructure collaboration. Where physical infrastructure is in an upstream nation, downstream impacts may be ignored after the structure has been completed. Using a game theoretic model, it is shown that downstream cooperation may only be rational when flooding is the primary downstream impact. A stylized arid developing region and humid developed region are compared. Potential gains from collaboration are greatest in arid regions, but may be difficult to achieve. There may be little scope for capturing the gains from basin level management if economic integration does not extend beyond water issues.

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.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.148
Teacher spread0.142 · 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