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Record W2136517737 · doi:10.1139/f02-070

Reconstructing the lives of fish using Sr isotopes in otoliths

2002· article· en· W2136517737 on OpenAlexvenueno aff
Brian P. Kennedy, Andrea Klaue, Joel D. Blum, Carol L. Folt, Keith H. Nislow

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsOtolithSalmoFish migrationHatcheryPopulationFisheryFish <Actinopterygii>Life historyIsotopes of strontiumEcologyEnvironmental scienceBiologyStrontiumChemistry

Abstract

fetched live from OpenAlex

For many species, understanding the processes underlying variation in life history strategies is limited by the difficulty of tracking individuals throughout their lives. Within the rapidly expanding field of otolith microchemistry, novel approaches are being combined with state-of-the-art analytical techniques to provide new and valuable information about the environmental history of fishes. However, no approach to date allows the reconstruction of fish movements at high temporal resolution (weeks to months) over relatively small spatial scales (1–10 km). We used micromilling techniques to extract strontium (Sr) isotopic signatures from the otoliths of four returning Atlantic salmon (Salmo salar) adults. Distinct Sr isotopic signatures were detectable from four life cycle stages, including prefeeding hatchery development, rearing stream growth, smolt out-migration, and ocean residence. High-resolution analyses of Sr isotope records establish that natal stream signatures are recoverable and show that both site fidelity within the freshwater stage and the timing of migration vary considerably among individuals. Results made possible with this approach provide insight into a long-standing debate on the mobility of salmon during their nonmigratory stage. The ability to resolve flexible behaviors of salmon increases our understanding of their population biology and conservation needs.

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 score0.482
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 spread0.197 · 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

Citations240
Published2002
Admission routes1
Has abstractyes

Explore more

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