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Record W2579649797 · doi:10.1139/cjfas-2016-0420

Comparing compound-specific and bulk stable nitrogen isotope trophic discrimination factors across multiple freshwater fish species and diets

2017· article· en· W2579649797 on OpenAlexvenueno aff
Chelsey M. Blanke, Yoshito Chikaraishi, Yuko Takizawa, Shawn A. Steffan, Prarthana S. Dharampal, M. Jake Vander Zanden

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
FundersWisconsin Sea Grant Institute, University of Wisconsin
KeywordsTrophic levelFood webStable isotope ratioIsotope analysisBiologyδ15NFreshwater fishEcologyIsotopes of nitrogenHabitatTaxonFish <Actinopterygii>Range (aeronautics)ZoologyNitrogenChemistryδ13CFishery

Abstract

fetched live from OpenAlex

Compound-specific nitrogen stable isotope analysis provides an approach for estimating animal trophic position that may overcome key issues associated with stable isotope analysis of bulk tissue. Yet compound-specific trophic discrimination factors have not been estimated for a broad range of habitats, taxa, and diets. We conducted a controlled-feeding experiment to characterize the variation in compound-specific (TDF AA ) and bulk (TDF Bulk ) trophic discrimination factors of four freshwater fish species fed on three distinct diets. We also compared TDF AA of fish muscle and scale to evaluate the viability of scales for making food web inferences. Mean ± 1 SD TDF Bulk was 2.2‰ ± 0.9‰, and there were significant effects of species and diet trophic position on TDF Bulk . Mean ± 1 SD TDF AA was 6.9‰ ± 0.8‰. Although there was no effect of species on TDF AA , there were significant differences in TDF AA across the three diets. TDF AA from fish scales were not significantly different from those of muscle. Our study illustrates the advantages of estimating trophic position using compound-specific stable isotopes and the need for continued investigation of factors resulting in variation in TDF values.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.999

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 studies0.0020.003
Scholarly communication0.0010.001
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 head0.043
GPT teacher head0.239
Teacher spread0.196 · 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

Citations49
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicIsotope Analysis in EcologyFrench-language works237,207