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Record W1980124673 · doi:10.1002/hyp.321

Regulation effects on the lower Peace River, Canada

2001· article· en· W1980124673 on OpenAlexafffundabout
Daniel L. Peters, Terry D. Prowse

Bibliographic record

VenueHydrological Processes · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsTrent UniversityImpact
FundersUniversity of Alberta
KeywordsHydrographTributaryHydrology (agriculture)Downstream (manufacturing)Environmental scienceInflowHydroelectricityFlow (mathematics)Surface runoffGeologyMeteorologyGeographyEcologyMathematicsGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract The headwaters of the Peace River, Canada became regulated in 1968 by a major hydroelectric facility and associated reservoir located in the Rocky Mountains. This paper examines the change to the downstream hydrographs that have resulted from regulation. To facilitate the comparison, a naturalized (without regulation effects) flow regime (1972–1996) was generated using a combination of hydrologic and hydraulic flow models. The results showed that even some 1100 km downstream, there have been significant changes to the hydrograph. Specifically, average winter flows were 250% higher, annual peaks (1‐day, 15‐day, 30‐day highs) were in the order of 35–39% lower, and overall variability in daily flows decreased. Despite the reduction in peaks and variability, however, the downstream hydrograph is far from flat and has retained the basic shape of the pre‐regulation hydrograph. This is primarily due to the strong influence of tributary inflow below the point of regulation. Recommendation for improvements to the model and future application of these data are also discussed. Copyright © 2001 John Wiley & Sons, Ltd.

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)0.001
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 score0.035
Threshold uncertainty score0.252

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.195
Teacher spread0.185 · 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

Citations105
Published2001
Admission routes3
Has abstractyes

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