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Record W2063523967 · doi:10.4296/cwrj3301023

Long-Term Water Level Changes in Closed-Basin Lakes of the Canadian Prairies

2008· article· en· W2063523967 on OpenAlexvenueaboutno aff
Garth van der Kamp, Dwayne Keir, Marlene S. Evans

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAridHydrology (agriculture)Structural basinEnvironmental sciencePrecipitationSurface runoffWater levelWater balanceDrainage basinClimate changePhysical geographyTerm (time)GeographyGeologyEcologyOceanography

Abstract

fetched live from OpenAlex

The semi-arid prairie region of Canada has many closed-basin lakes that are sensitive to climatic variability and change. Long-term water level changes in these lakes provide a measure of the dynamic balance between runoff and precipitation supplying water to the lakes and water loss from the lakes by evaporation. Historic lake water level data can help to improve understanding and prediction of the hydrologic effects of climate change and land-use changes. Water level data for sixteen closed-basin lakes in the Canadian prairies were compiled from a variety of sources and additional measurements were made at some locations. In the Canadian prairie region there is an overall pattern throughout most of the twentieth century of declining lake levels, although with notable exceptions. Possible causes of lake level changes are assessed briefly, but the main objective is to present a regional and long-term perspective on lake level records.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.024
GPT teacher head0.200
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations100
Published2008
Admission routes2
Has abstractyes

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