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Record W2737048685 · doi:10.1139/cjce-2017-0141

Hydropower plant adaptation strategies for climate change impacts on hydrological regime

2017· article· en· W2737048685 on OpenAlexaffvenueabout
Didier Haguma, Robert Leconte, Stéphane Krau

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsHydropowerInflowEnvironmental scienceAdaptation (eye)Climate changeWater resourcesSmall hydroWater resource managementEnvironmental resource managementEngineeringEcologyMeteorologyGeography

Abstract

fetched live from OpenAlex

This study addresses the problem of the adaptation of hydropower plants to the future climate. A water resources optimization method was used to determine the optimum operating policy for the Manicouagan water system, Quebec, Canada, for the future hydrological regime and to establish hydropower adaptation strategies. The adaptation of the operating policy showed an increase in hydropower generation, an increase in unproductive spills, and a decrease in hydropower plant efficiency as consequences of the inflow increase, especially in winter and spring. Structural adaptation strategies, which consisted of either hydropower plant expansion or refurbishment, were analyzed. The plant expansion strategy was completely unsuccessful because the increase in inflow year-round in the future climate was too low to economically justify the installation of additional turbine-generator units. The refurbishment strategy was found to be a feasible structural adaptation measure for the Manicouagan system, under certain favorable economic and hydropower plant conditions.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.029
GPT teacher head0.225
Teacher spread0.196 · 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 designSimulation or modeling
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

Citations19
Published2017
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Civil EngineeringSame topicHydrology and Watershed Management StudiesFrench-language works237,207