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Record W2517543316 · doi:10.1139/cjfas-2016-0110

Selective pressure of fishways upon morphological and muscle enzymatic traits of migrating glass eels

2016· article· en· W2517543316 on OpenAlexvenueno aff
Tomasz Podgorniak, M. Angelini, Éric De Oliveira, Françoise Daverat, Fabien Pierron

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsClimbingAnguillidaeHabitatBiologyEnergy metabolismFish <Actinopterygii>EcologyFisheryZoologyEndocrinology

Abstract

fetched live from OpenAlex

Fishways are built on obstacles to restore the connectivity between aquatic habitats. Our study investigated how species-specific fishways interact with interindividual trait differences among fish. We compared several groups of glass eels (Anguilla anguilla) climbing two types of fishways with those remaining below the water impoundments. We analyzed relative body condition factor, body length, mass, and in vitro activities and gene transcription levels of several enzymes involved in the energy metabolism (LDH, CS, CCO, PK). Differences among groups indicated that glass eel fish ladders can be size-specific, according to the type of substrate covering the surface of apparatus. Moreover, we found that climbing specific glass eel ladders can require higher endurance-related metabolic traits, triggering increased aerobic capacities. Increased aerobic efforts together with potential size selectivity of fishways should be taken into account when designing such devices and choosing appropriate location on eel migratory pathways.

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)0.000
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.014
GPT teacher head0.200
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

Citations8
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic Sciences→Same topicFish Ecology and Management Studies→French-language works237,207→