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Record W2039576014 · doi:10.1139/f01-176

Zebra mussels (<i>Dreissena polymorpha</i>), habitat alteration, and yellow perch (<i>Perca flavescens</i>) foraging: system-wide effects and behavioural mechanisms

2001· article· en· W2039576014 on OpenAlexvenueno aff
Carola Mayer, Lars G. Rudstam, Edward L. Mills, Scott G. Cardiff, Carly Anne Bloom

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsDreissenaZebra musselPerchBiologyForagingPredationMusselEcologyAbiotic componentFisheryBivalviaMolluscaFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The aggregate impact of an exotic species introduction, such as the zebra mussel (Dreissena polymorpha), may involve a large number of biotic and abiotic interactions within the recipient ecosystem. We used laboratory experiments and field data to assess effects of zebra mussels on both foraging success of yellow perch (Perca flavescens) and activity of the amphipod Gammarus fasciatus. In two laboratory experiments zebra mussel clusters reduced the rate at which yellow perch captured amphipods. Yellow perch captured fewer amphipods when zebra mussels were present at two light levels (&lt;2.1 and &gt;214 lx) and across a range of prey densities (76–1500 amphipods·m –2 ). The effect of zebra mussels on amphipod activity depended on light level. Yellow perch captured fewer amphipods in the presence of mussel clusters than when plants were present. The frequency of amphipods in the diets of adult yellow perch in Oneida Lake increased after zebra mussel introduction, but the increase was greater in low mussel density habitats. Our laboratory results and field observations suggest that zebra mussels affect yellow perch foraging on amphipods through increased structural complexity (negative) and increased light penetration ( positive), but not through increased prey density.

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 score0.027
Threshold uncertainty score0.985

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 studies0.0010.001
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.200
Teacher spread0.188 · 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

Citations70
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicAquatic Invertebrate Ecology and BehaviorFrench-language works237,207