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Record W1968614438 · doi:10.1139/f99-174

Changes in isotopic composition of red drum (<i>Sciaenops ocellatus</i>) larvae in response to dietary shifts: potential applications to settlement studies

2000· article· en· W1968614438 on OpenAlexvenueno aff
Sharon Z. Herzka, G. Joan Holt

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsFractionationLarvaBiologyComposition (language)OntogenyStable isotope ratioDemersal zonePlanktonEcologyMetamorphosisAnimal scienceZoologyFish <Actinopterygii>FisheryChemistry

Abstract

fetched live from OpenAlex

The stable isotope composition of larval fish tissues may serve as a chemical tracer of recent settlement due to food web differences among planktonic and demersal habitats. We present the background for the utilization of δ 13 C and δ 15 N to trace settlement of red drum (Sciaenops ocellatus), an estuarine-dependent species. The effect of ontogeny and temperature on the relative contribution of growth and metabolic turnover to changes in isotopic composition was examined by simulating dietary shifts in the laboratory. Fractionation was examined as a function of size and the effect of food deprivation was evaluated. Published growth rates were used to estimate the time period within which the isotopic composition of a new food source should be reflected in larval tissues. In response to dietary shifts, larvae exhibited quick changes in δ 13 C and δ 15 N in a pattern closely resembling predictions based on growth alone. Fractionation values were about +1‰ for δ 13 C and +1.6‰ for δ 15 N. There was no effect of 4 days of food deprivation on δ 13 C and δ 15 N. Given the fast growth rates reported for newly settled red drum, their isotopic composition should exhibit a shift within 1-2 days and stabilize about 10 days following settlement.

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 score0.244
Threshold uncertainty score0.767

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.246
Teacher spread0.229 · 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

Citations180
Published2000
Admission routes1
Has abstractyes

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