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Record W2074472963 · doi:10.1577/m07-117.1

The Role of Ciscoes as Prey in the Trophy Growth Potential of Walleyes

2009· article· en· W2074472963 on OpenAlexafffundabout
Scott D. Kaufman, George Emir Morgan, John M. Gunn

Bibliographic record

VenueNorth American Journal of Fisheries Management · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsLaurentian University
FundersDivision of Ocean SciencesNatural Sciences and Engineering Research Council of CanadaOntario Federation of Anglers and HuntersMinistry of Natural ResourcesCisco Systems
KeywordsPredationPerchTrophyFisheryBiologyFish <Actinopterygii>InvertebrateEcologyGeography

Abstract

fetched live from OpenAlex

Abstract Analysis of the growth characteristics of 215 populations of walleye Sander vitreus across Ontario revealed that female walleyes reached larger asymptotic lengths in lakes in which ciscoes Coregonus artedi provided a relatively large prey species for them. The stomach contents of walleyes from a set of intensively studied lakes revealed that walleyes of all sizes depended on ciscoes but that ciscoes were most important to larger walleyes. In lakes without ciscoes, the walleye diet closely tracked the availability of young-of-year yellow perch Perca flavescens; when such prey were in short supply, walleyes consumed invertebrates. The prey size in walleye stomachs was significantly larger in lakes with ciscoes, but the probability of finding empty stomachs was also greater. This suggests that although walleyes in lakes with ciscoes foraged less often, more energy was available for growth. Exceptions to this general pattern occurred when ciscoes were too large to be consumed by walleyes or ciscoes were absent but small prey (i.e., yellow perch) were very abundant. We propose that lakes without ciscoes will provide higher catch rates for anglers, whereas those with ciscoes (particularly small ciscoes) are more likely to provide opportunities for trophy size fish.

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 score0.161
Threshold uncertainty score0.285

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 science0.0010.000
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 head0.002
GPT teacher head0.178
Teacher spread0.176 · 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

Citations42
Published2009
Admission routes3
Has abstractyes

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