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Record W2036625874 · doi:10.1139/f05-217

Using bioenergetics and stable isotopes to assess the trophic role of rusty crayfish (<i>Orconectes rusticus</i>) in lake littoral zones

2006· article· en· W2036625874 on OpenAlexvenueno aff
Brian M. Roth, Catherine L. Hein, M. Jake Vander Zanden

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
FundersUniversity of California, DavisNational Science Foundation
KeywordsCrayfishTrophic levelLittoral zonePredationEcologyForagingBiologyIsotope analysisEcosystemTrophic cascadeFood web

Abstract

fetched live from OpenAlex

Crayfish often dominate freshwater ecosystems and can have strong effects on littoral habitat and biota through foraging. However, there is substantial debate regarding the trophic role of crayfish and implications of their foraging behavior on littoral ecosystems. We created a bioenergetics model for rusty crayfish (Orconectes rusticus) to determine how growth and prey choice constrain crayfish consumption, which affects littoral ecosystems. We simulated over 6400 potential rusty crayfish diets by varying the abundance of important prey. We judged how crayfish prey choice affects the amount and identity of prey that crayfish must consume to match observed growth. We compared our model predictions with stable isotope evidence from 10 northern Wisconsin lakes to determine energy sources and trophic position of rusty crayfish relative to other invertebrates. Our diet simulations and stable isotope analysis concurred that crayfish of all ages are primarily predators. Individuals that do not feed on zoobenthos must therefore consume large quantities of less desirable food such as macrophytes, which are an important component of littoral habitat.

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 score0.408
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.224
Teacher spread0.205 · 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

Citations53
Published2006
Admission routes1
Has abstractyes

Explore more

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