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Record W2189618025 · doi:10.1139/f2012-001

Spatial and seasonal variability in the diet of round goby (<i>Neogobius melanostomus</i>): stable isotopes indicate that stomach contents overestimate the importance of dreissenids

2012· article· en· W2189618025 on OpenAlexafffundvenueabout
Jaclyn M. Brush, Aaron T. Fisk, Nigel E. Hussey, Timothy B. Johnson

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsMinistry of Natural Resources and ForestryUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Natural Resources
KeywordsRound gobyNeogobiusTrophic levelBenthic zoneBayLittoral zoneEcologyBiologyPredationIsotope analysisFisheryOceanographyGeology

Abstract

fetched live from OpenAlex

Our results provide new information that diet, carbon source and trophic position of an invasive fish species, round goby ( Neogobius melanostomus ), varies seasonally, spatially and with body size in littoral habitats of Lake Ontario. Based on stomach contents and stable isotopes, round goby fed at a higher trophic position in the cooler, less productive Kingston Basin relative to the Bay of Quinte. Bay of Quinte round goby were more reliant on terrestrial carbon, whereas littoral carbon dominated in the Kingston Basin. Although stomach contents suggested dreissenids were the dominant prey item of round goby, stable isotope mixing models estimated that dreissenids were never &gt;39% and 11% of the diet in Bay of Quinte and Kingston Basin, respectively. Stable isotopes indicated amphipods, chironomids and cladocerans were the most important prey, and were at times common items in stomach contents, but this varied with site, season and year. Given their high abundance, the impact of round gobies on the benthic biodiversity of the Great Lakes may be more significant than indicated by stomach content analysis alone.

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)0.003
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 score0.071
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.019
GPT teacher head0.230
Teacher spread0.211 · 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

Citations100
Published2012
Admission routes4
Has abstractyes

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