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Record W1549766256

Five Views of the Great Lakes and Why They Might Matter

2006· article· en· W1549766256 on OpenAlexaboutno aff
A. Dan Tarlock

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsStructural basinTreatyGeographyWatershedCommissionDrainage basinHydrology (agriculture)LawPolitical scienceGeologyCartography
DOInot available

Abstract

fetched live from OpenAlex

Compared to many of the world's contested international watersheds such as the Amu Darya in Central Asia, the Colorado River, or the Nile Basin, the Canadian-United States Great Lakes Basin is a paradox: the level of controversy about the management and use of the waters is inverse to the amount of water in the basin. The lakes themselves contain twenty percent of the world's fresh water). However, comparatively little of this water is currently withdrawn, and only about five percent of that amount is consumed and not returned to the watershed. In 2002, the International Joint Commission (IJC), the Canadian-United States body which administers the 1909 Boundary Waters Treaty, revised its previous consumptive use estimates downward by eighteen percent. Out-of-basin diversions are even smaller. The major transbasin diversion is the Chicago diversion. Chicago and its lakeshore suburbs are authorized by a United States Supreme Court decree to with-draw 3,200 cubic feet per second from Lake Michigan. Only the fact that the Mississippi watershed encompasses most of the metropolitan Chicago area makes this a transbasin diversion. The other major transwatershed diversion, the Long Lake and Ogoki diversions, actually add water to Lake Superior. However, despite the modest levels of present and projected consumptive use and the vast amount of water in the lakes, fears about future in-basin consumptive uses and transbasin diversions have been a major political and legal issue in the basin for more than two decades.

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 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 score0.683
Threshold uncertainty score0.992

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)0.0010.000

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 head0.013
GPT teacher head0.239
Teacher spread0.226 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2006
Admission routes1
Has abstractyes

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