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Protecting aquatic ecosystems in heavily allocated river systems: the case of the Oldman River Basin, Alberta

2010· article· en· W2156816392 on OpenAlexaffvenueabout
Bryan A. Poirier, Rob C. de Loë

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAquatic ecosystemEcosystemCLARITYWatershedEnvironmental resource managementFreshwater ecosystemDrainage basinEnvironmental planningBusinessWater resource managementGeographyEnvironmental scienceEcologyComputer science

Abstract

fetched live from OpenAlex

Initiatives aimed at protecting aquatic ecosystems often prove difficult to implement—particularly in water‐stressed, semi‐arid regions where the demand for water for human consumption is high. This article reports on an investigation of the factors that shaped the development and implementation of policies for aquatic ecosystem protection in the Oldman River Basin (ORB), a crucial watershed in semi‐arid southern Alberta. The analysis reveals critical cultural and historical considerations that confront those attempting to protect and restore aquatic ecosystems in the ORB and highlights specific factors that influence implementation of measures to protect it. In addition to the most basic consideration—demand for water exceeds supplies—eight specific factors that influenced aquatic ecosystem protection in this region are identified and evaluated. These include (1) clarity of actors’ roles/ jurisdictional responsibilities; (2) communication; (3) definition of key terms; (4) funding and organizational capacity; (5) leadership; (6) legal standing; (7) data and monitoring; and (8) public education. We argue that while these factors are important, critical cultural and historical considerations that influence water policy in the province also must be addressed in any efforts to protect aquatic ecosystems in southern Alberta.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.012
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.161
Teacher spread0.157 · 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 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
Published2010
Admission routes3
Has abstractyes

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