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Record W2472502490 · doi:10.1002/eco.1758

Differences in movements of northern pike inhabiting rivers with contrasting flow regimes

2016· article· en· W2472502490 on OpenAlexaffabout
Simonne Harvey‐Lavoie, Steven J. Cooke, Guillaume Guénard, Daniel Boisclair

Bibliographic record

VenueEcohydrology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton UniversityUniversité de MontréalCégep Marie-Victorin
Fundersnot available
KeywordsPikeEsoxHydrology (agriculture)Environmental scienceHabitatStreamflowFisherySTREAMSFish <Actinopterygii>GeographyEcologyDrainage basinGeologyBiology

Abstract

fetched live from OpenAlex

Abstract Little is known about the effect of short‐term flow changes on the movement of northern pike ( Esox lucius L.). We conducted a comparative study in two rivers with different flow regimes (i.e. one regulated and one unregulated) to evaluate the extent to which adult northern pike movement is influenced by short‐term river flow conditions. Data on northern pike hourly movement were collected in northeastern Ontario on 40 individuals in the Mississagi River, a river subjected to extreme daily flow variations associated with hydro‐electric operations (i.e. hydropeaking), and the Aubinadong River, an unregulated river. Using manual tracking by radio‐telemetry, we found that northern pike moved at a higher rate in the river subjected to short‐term changes in river flow than in the unregulated river. Results also show that the relationship between movement of northern pike and flow can vary among rivers. In the Mississagi River, flow had a significant positive effect on northern pike activity, while the effect of flow in the Aubinadong River depended on water temperature. Moreover, fish in the Mississagi River utilized a restricted river section and travelled more laterally in comparison to fish inhabiting the Aubinadong River, where longer upstream‐downstream movements were more common. The present study contributes to our understanding of the effects of short‐term changes in flow on fish movement. This constitutes the first step towards assessing the effect of flow on fish habitat use, and eventually developing flow management strategies to mitigate impacts on fish. Copyright © 2016 John Wiley &amp; Sons, Ltd.

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 score0.012
Threshold uncertainty score0.758

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)0.0010.000

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 head0.006
GPT teacher head0.177
Teacher spread0.171 · 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

Citations11
Published2016
Admission routes2
Has abstractyes

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