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Record W4394784519 · doi:10.1007/s10750-024-05530-5

Downstream passage performance of silver eel at an angled rack: effects of behavior and morphology

2024· article· en· W4394784519 on OpenAlexaff
Roman Motyka, Johan Watz, David Aldvén, Niclas Carlsson, Felix Eissenhauer, Andrew Harbicht, Eirini Karathanou, Tobias Knieps, Lovisa Lind, Olle Calles

Bibliographic record

VenueHydrobiologia · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
FundersEnergimyndighetenKarlstads universitetVattenfall
KeywordsEnvironmental scienceHydropowerEcologyFisheryBiology

Abstract

fetched live from OpenAlex

Abstract The European eel is critically endangered due to heavy impact of anthropogenic factors, such as habitat fragmentation, overexploitation and climate change. During downstream migration, silver eels may encounter hydropower plants, which often result in delay or mortality from impingement on trash-racks or turbine passage. These problems can be mitigated with downstream passage solutions, such as angled racks that guide downstream-migrating eels to safe passage routes. The importance of bar spacing and phenotypic diversity for passage performance is, however, largely unknown. In this study, we investigated how morphological parameters (body mass, eye and fin indices) and behavioral score (open field test) influenced passage rate at an experimental intake equipped with a bypass and angled racks with either 15 or 30 mm bar spacing. Both racks were efficient in guiding eels into a bypass. There was a strong positive effect of body mass and a weak positive effect of open field test score on passage rate. Other factors such as eye and fin indices played a minor role. These results demonstrate the performance of angled racks with bypasses and form a useful starting point for further research regarding the relationships between individual variation in behavior, morphology and passage solutions for silver eels.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.590

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 studies0.0000.001
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 head0.005
GPT teacher head0.206
Teacher spread0.201 · 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 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

Citations3
Published2024
Admission routes1
Has abstractyes

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