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The effect of competition among three salmonids on dominance and growth during the juvenile life stage

2012· article· en· W2013394138 on OpenAlexafffund
Jessica A. Van Zwol, Bryan D. Neff, Chris C. Wilson

Bibliographic record

VenueEcology Of Freshwater Fish · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMinistry of Natural Resources and ForestryWestern University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Federation of Anglers and HuntersWorld Wildlife Fund
KeywordsBrown troutRainbow troutSalmoJuvenileFisheryBiologyTroutCompetition (biology)EcologyDominance (genetics)Fish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract Although non‐native species can sometimes threaten the value of ecosystem services, their presence can contribute to the benefits derived from the environment. In the G reat L akes, non‐native brown trout ( S almo trutta ) and rainbow trout ( O ncorhynchus mykiss ) support substantial recreational fisheries. With current efforts underway to restore once‐native A tlantic salmon ( S almo salar ) to L ake O ntario, there is some concern that A tlantic salmon will impede non‐native contributions to the recreational fishery because A tlantic salmon exhibit niche overlap with brown trout and rainbow trout, particularly during the juvenile life stage. We therefore examined competition and growth of juvenile A tlantic salmon, brown trout and rainbow trout in semi‐natural streams. We found that brown trout were the most dominant and had the greatest growth rate regardless of what other species were present. Rainbow trout were more dominant than A tlantic salmon and consumed the most food of the three species. However, in the presence of brown trout, rainbow trout fed less frequently and exhibited negative growth as compared to when the rainbow trout were present with only A tlantic salmon. These data suggest that, outside of density‐dependent effects, Atlantic salmon will not impact stream production of brown trout and rainbow trout.

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)0.001
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 score0.015
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.004
GPT teacher head0.185
Teacher spread0.182 · 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

Citations30
Published2012
Admission routes2
Has abstractyes

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