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Record W2108674986 · doi:10.1139/f01-118

Diet of <i>Mysis relicta</i> in Lake Ontario as revealed by stable isotope and gut content analysis

2001· article· en· W2108674986 on OpenAlexvenueaboutno aff
Ora E. Johannsson, M F Leggett, Lars G. Rudstam, Mark R. Servos, MA Mohammadian, Gideon Gal, Ron M. Dermott, Ray H. Hesslein

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsTrophic levelPredationZooplanktonIsotope analysisBiologyPhytoplanktonEcologyMysidaceaStable isotope ratioBioenergeticsFood webCopepodCrustaceanNutrient

Abstract

fetched live from OpenAlex

Stable isotope analysis of the potential prey and predator can be combined with gut content analysis to quantify the diet. This dietary knowledge allows the quantitative assessment of the role of key species in energy and contaminant transfer, their impact on prey communities, and their susceptibility to perturbation. The diet of Mysis relicta was examined in Lake Ontario in spring, summer, and autumn using both techniques. Mysids fed on the bottom during the day and in the pelagia and on the bottom at night. A trophic fractionation of 2.2‰ N between mysids and their prey provided the best correspondence between the observed stable isotope signature of mysids and that estimated from their diet. Tissue turnover rate of δ 13 C was slow compared with that of δ 15 N. Diatoms formed 50% of the assimilated diet in May. In September, 25% of large mysids feeding on the bottom contained amphipod parts and 20% contained phytoplankton. The remainder of the diet consisted of zooplankton and rotifers. The contribution of amphipods and phytoplankton could not be quantified. Revised daily consumption estimates, based on this new diet information and clearance rate estimates of consumption, gave daily consumption estimates similar to those estimated from previous bioenergetic modelling.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.998

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)0.0030.000

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 head0.210
Teacher spread0.191 · 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.

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

Citations122
Published2001
Admission routes2
Has abstractyes

Explore more

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