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Record W2077779784 · doi:10.1577/t06-070.1

Dynamics of Piscivory by Lake Trout following a Smallmouth Bass Invasion: A Historical Reconstruction

2007· article· en· W2077779784 on OpenAlexafffundabout
Yolanda E. Morbey, Kris Vascotto, Brian J. Shuter

Bibliographic record

VenueTransactions of the American Fisheries Society · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMinistry of Natural Resources and ForestryUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoMinistry of Natural Resources
KeywordsMicropterusCoregonus clupeaformisTroutSalvelinusCoregonusFisheryForage fishPelagic zonePiscivorePerchBass (fish)PredationBiologyEcologyFish <Actinopterygii>Predator

Abstract

fetched live from OpenAlex

Abstract Our objective was to assess the dynamics of piscivory by lake trout Salvelinus namaycush in Lake Opeongo, Ontario, following the introduction of smallmouth bass Micropterus dolomieu early in the 1900s. The effects of this introduction on lake trout were thought to be of slight significance at the time, but they may have been obscured by the introduction of cisco Coregonus artedi in 1948. Our analyses of lake trout stomach contents and stable isotopes of archived scales indicated that several dietary changes occurred in advance of the cisco introduction. These changes included the consumption of fewer yellow perch Perca flavescens, fewer but larger lake whitefish C. clupeaformis, and larger lake whitefish for a given lake trout size. Stable isotope analyses were consistent with a decline in the importance of littoral prey for young lake trout before the introduction of cisco. We hypothesize that the indirect effects of smallmouth bass on the pelagic fish community explain these patterns.

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)0.001
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 score0.145
Threshold uncertainty score0.288

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.008
GPT teacher head0.196
Teacher spread0.187 · 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

Citations20
Published2007
Admission routes3
Has abstractyes

Explore more

Same venueTransactions of the American Fisheries SocietySame topicFish Ecology and Management StudiesFrench-language works237,207