MétaCan
Menu
Back to cohort
Record W2120669484 · doi:10.1139/f06-076

Stable hydrogen isotopic composition of fishes reflects that of their environment

2006· article· en· W2120669484 on OpenAlexvenueno aff
Gregory W. Whitledge, Brett M. Johnson, Patrick J. Martinez

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsOtolithFish <Actinopterygii>MicrochemistryBiologyEcologyHydrogen isotopeFreshwater fishFisheryEnvironmental scienceHydrogenChemistry

Abstract

fetched live from OpenAlex

Otolith microchemistry and isotopic analyses have emerged as effective techniques for providing insights into fish environmental history that are difficult to obtain by other means. Stable hydrogen isotope ratio ( 2 H/ 1 H or D/H, expressed as δD) is a possible environmental marker that has not been employed in fish provenance research, although it has been applied as a natural tracer of terrestrial organism migrations. We illustrate the potential of δD to serve as a new natural marker of fish environmental history by demonstrating that significant linear relationships (r 2 ≥ 0.97) exist between fish otolith and muscle δD and δD of waters that fish inhabit. Differences between mean water δD and both muscle and otolith δD were not significantly correlated with fish total length and were not significantly different among species, indicating that water–fish δD relationships are consistent across fish sizes and species. High r 2 values for regressions of otolith and muscle δD on water δD for fishes inhabiting locations with diverse thermal regimes suggest that relationships between water and fish δD are not strongly affected by water temperature. Demonstration that fish δD clearly reflects water δD provides a foundation for future research to reconstruct fish movement among locations with distinct δD signatures.

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 score0.140
Threshold uncertainty score0.998

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 communication0.0000.000
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 head0.014
GPT teacher head0.197
Teacher spread0.182 · 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

Citations50
Published2006
Admission routes1
Has abstractyes

Explore more

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