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Record W3131603849 · doi:10.1051/kmae/2021004

Temporary turbine and reservoir level management to improve downstream migration of juvenile salmon through a hydropower complex

2021· article· en· W3131603849 on OpenAlexfundno aff
Stéphane Tetard, Romain Roy, Nils Teichert, Jocelyn Rancon, Dominique Courret

Bibliographic record

VenueKnowledge and Management of Aquatic Ecosystems · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersOffice Français de la BiodiversitéEuropean CommissionMcGill University
KeywordsHydropowerEnvironmental scienceTurbineJuvenileDownstream (manufacturing)Fish migrationFisheryHydrology (agriculture)Marine engineeringFish <Actinopterygii>Operations managementEcologyEngineeringBiologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Developing management rules to improve downstream migration of salmon smolts in large hydropower plants is essential to limit mortality and migration delay. A 2-year telemetry study was conducted to assess the efficiency of temporary measures to enhance the safety and speed of juvenile salmon passage through the Poutès dam (Allier River, France). 124 smolts were tracked through the reservoir and downstream of the dam, during implementation of turbine modulation and/or shutdown during night and reservoir level lowering. Level lowering significantly reduced median residence time from 3.4 days to 4.4 hours. However, even with high spill during turbine modulation, the risk of smolt being drawn toward the turbines was increased at low reservoir level due to the site's configuration, greater proximity to the surface and weak repulsive effect of the rack. Moreover, results revealed that a substantial proportion of smolts can migrate during daytime and twilight during floods, even at the beginning of the migration period. Thus targeted turbine shutdown has a good potential to protect smolts, but implementation requires studies taking account of site specificities and a flexible approach.

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 score0.294
Threshold uncertainty score0.800

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.257
Teacher spread0.232 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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