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Record W1988609840 · doi:10.1139/f04-231

Differential isotopic enrichment and half-life among tissues in Japanese temperate bass (<i>Lateolabrax japonicus</i>) juveniles: implications for analyzing migration

2005· article· en· W1988609840 on OpenAlexvenueno aff
Keita W. Suzuki, Akihide Kasai, Kouji Nakayama, Masaru Tanaka

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
FundersH2020 European Research Council
KeywordsLateolabraxTrophic levelPerciformesBass (fish)BiologyStable isotope ratioTemperate climateIsotopeAnimal scienceChemistryFisheryEcologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

As a first step for field applications of stable isotope techniques to investigate the migration of Japanese temperate bass (Lateolabrax japonicus) (Perciformes) juveniles, we conducted a diet switch experiment and fitted an exponential model to changes in stable carbon (δ 13 C) and nitrogen (δ 15 N) isotope ratios for muscle, fin, and liver. The trophic enrichment values were ranked liver &lt; muscle &lt; fin for δ 13 C (range –0.80‰ to +3.66‰) and liver &lt; fin &lt; muscle for δ 15 N (+0.59‰ to +3.12‰). The half-life values were similar for muscle and fin for both δ 13 C and δ 15 N (19.3–25.7 days), while those for liver were 5.3 days for δ 13 C and 14.4 days for δ 15 N. Both the δ 13 C and δ 15 N values of muscle reached the asymptotic value after a threefold body weight increase, reflecting the diet after the switch. These results suggest that fin is a useful substitute for muscle in field applications of stable isotope techniques and that liver, with a shorter half-life, has the potential to provide more recent information about migration.

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 score0.382
Threshold uncertainty score0.995

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 communication0.0000.001
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 head0.013
GPT teacher head0.234
Teacher spread0.222 · 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

Citations156
Published2005
Admission routes1
Has abstractyes

Explore more

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