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Finding a way upstream: environmental effects on immature American eel movement below a hydropower dam

2025· article· en· W7084762329 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Penology
Canadian institutionsnot available
Fundersnot available
KeywordsHydropowerTailwaterHabitatHydroelectricityUpstream and downstream (DNA)Upstream (networking)EcosystemFragmentation (computing)Downstream (manufacturing)

Abstract

fetched live from OpenAlex

The abundance of the American eel is declining, in part due to the fragmentation of riverine habitat by human-made barriers like hydroelectric dams. Despite the importance of successful passage at barriers during upstream migration, little is known about the movement behavior of immature eels at hydroelectric dams and the potential effects of artificial environmental conditions. Here, we investigate the movement behavior of migrating yellow eel below the Carillon dam, the first barrier during their upstream migration in the Ottawa River (Canada). Using acoustic telemetry, we quantify individual variability in movement behavior, timing of arrival at the dam, retention near the structure, return rates downstream, and whether eels find the only passage route to upstream habitats under fluctuating environmental conditions. Our results highlight intraspecific variability of exploratory behavior downstream of the Carillon dam and effects of atmospheric pressure, water flow, and temperature. On average, eels spent 17.7 days near the structure, but none successfully passed the dam, despite some individuals (16%) attempting passage multiple times. Thus, the Carillon dam likely represents an important barrier to immature eels during upstream migration that potentially confounds environmental migratory cues, induces migratory delay, and ultimately reduces access to headwater habitat.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.999

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0380.002

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 head0.016
GPT teacher head0.311
Teacher spread0.296 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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