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Record W2789669939 · doi:10.1002/rra.3256

Diel activity and foraging mode of juvenile Arctic charr in fluctuating water flow

2018· article· en· W2789669939 on OpenAlexaff
Nicolas Larranaga, Sveinn K. Valdimarsson, Tommi Linnansaari, Stefán Ó. Steingrímsson

Bibliographic record

VenueRiver Research and Applications · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of New Brunswick
FundersIcelandic Centre for ResearchLandsvirkjun
KeywordsDiel vertical migrationForagingNocturnalPredationEnvironmental scienceJuvenileEcologyPopulationArcticHabitatBiologyFlow (mathematics)Physics

Abstract

fetched live from OpenAlex

Abstract Streams fluctuate in water flow because of natural (e.g., rain) and human‐induced events (e.g., hydropeaking). Magnitude, frequency, and predictability of these events can have drastic consequences for fish populations. We studied how rapid modifications of water flow affect diel activity and foraging mode of juvenile Arctic charr Salvelinus alpinus in stream enclosures exposed to either stable (intermediate) or fluctuating (low vs. high) water flow. Under stable conditions, Arctic charr showed limited activity (9.4%). In fluctuating water flow, charr increased activity during low flow periods, especially during the first hours after the flow decreased, but ceased activity almost completely at high flow. Charr were mostly nocturnal, and more nocturnal at low than intermediate water flow. Fish were more mobile and swam faster during prey search and attacked prey at longer distances at low water flow. Activity and foraging mode differed between the first and second day after reduced water flow, suggesting that Arctic charr require time to adjust their foraging behaviour. This study demonstrates the importance of behavioural flexibility for population ecology in fluctuating environments such as regulated rivers.

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 score0.090
Threshold uncertainty score0.250

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)0.0000.000

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 head0.028
GPT teacher head0.320
Teacher spread0.292 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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