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Record W2257256376

Interjurisdictional water quantity issues in North America;s great lakes region

2000· article· en· W2257256376 on OpenAlexaboutno aff
Doug Cuthbert

Bibliographic record

Venue10th World Water Congress: Water, the Worlds Most Important Resource · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsHydropowerApportionmentGeographyWater supplyPopulationSustainabilityWater resourcesRecreationInternational watersResource (disambiguation)Climate changeEnvironmental protectionFisheryEnvironmental scienceOceanographyEcologyPolitical scienceEnvironmental engineering
DOInot available

Abstract

fetched live from OpenAlex

North America's Great Lakes-St. Lawrence River system contains about 18 percent of the world's freshwater supply. Yet water deficiencies, apportionment of water among competing interests, concerns over falling lake levels, and water export are becoming issues in this seemingly water rich region. Shared by two countries, two Canadian provinces, eight U.S. States and about 40 million people, the Great Lakes-St. Lawrence River region has been an industrial heartland of North America for two centuries. A lack of jurisdictional concern for water quantity issues as a result of abundant water for historical uses such as hydropower, inland and overseas shipping, commercial and recreational fisheries, and water supply for large population centres is now beginning to change. Current thinking and the necessity for ecosystem management, conservation sustainability and sharing the waters of the world is leading to a range of resource use and jurisdictional issues for North America's Great Lakes-St. Lawrence River region as we begin the 21st Century. How Canadians and Americans deal on an interjurisdictional basis with climate change, water use and apportionment issues, water export, water levels and flows regulation, and water diversions will stretch the talents and abilities of these two countries to cooperate and resolve conflicts. The goal is to maintain and enhance the benefits that the Great Lakes bring to the region, to the two countries, and to the people that share this invaluable water resource.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.014
GPT teacher head0.255
Teacher spread0.242 · 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; both teacher heads agree on what is shown here.

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
Published2000
Admission routes1
Has abstractyes

Explore more

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