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Record W7043583628

Transboundary Surface Water Management Framework forCooperation

2008· article· en· W7043583628 on OpenAlexaboutno aff

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicWater Resources and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsWatershedTributaryWatershed managementWater qualityWater resourcesAgricultureGovernment (linguistics)Flooding (psychology)
DOInot available

Abstract

fetched live from OpenAlex

Differing governmental organization and management strategies between Canada and the United States concerning watershed management is a cause for variance in resource management strategies. This paper discusses ongoing research investigating past transboundary watershed initiatives and current management schemes involved in transboundary watershed management. Individual stakeholders, Non-governmental organizations and all levels of government should collaborate to promote cooperation when designing and implementing transboundary watershed policy. Population expansion and urban development in Aldergrove and Abbotsford, British Columbia (B.C.) are impacting the Canadian portions of Bertrand and Fishtrap Creeks watersheds. As a result, flows have been altered from historical norms. Both watercourses and their tributaries provide irrigation and domestic water for residents in both Washington (WA) and B.C. Ground and surface water irrigation from these watersheds supports the agriculture industry in the Fraser Lowlands, a strong economic driver in the region. Interviews with local watershed regulators have indicated that instances of flooding during the rainy season have increased, while flows are below normal during the dry season. Water quality has also been compromised. Can low flows and questionable water quality act as drivers for cooperative transboundary management between watershed users, and regulators in WA and B.C.?

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.024
GPT teacher head0.238
Teacher spread0.214 · 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.

Study designNot applicable
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

Citations0
Published2008
Admission routes1
Has abstractyes

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