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Record W1997268230 · doi:10.1139/f2012-058

Estuarine ingress timing as revealed by spectral analysis of otolith life history scans

2012· article· en· W1997268230 on OpenAlexvenueno aff
Renee Reilly Hoover, Cynthia M. Jones, C. E. Grosch

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
FundersNational Marine Fisheries ServiceNational Science Foundation
KeywordsOtolithEstuaryJuvenileLife historyEnvironmental scienceOceanographyEcologyFish <Actinopterygii>FisheryBiologyGeology

Abstract

fetched live from OpenAlex

The ability to accurately measure the timing of migration is fundamental in testing hypotheses in marine ecology that deal with migration and movement of fish populations. Timing and patterns of movement in larval and juvenile fish have been estimated using life history scans of the chemical signatures encoded in their otoliths. We provide a quantitative approach to analyzing life history scan data using spectral analysis, which retrospectively measures the timing of ingress for individual fish. Saggital otoliths from juvenile Atlantic croaker ( Micropogonias undulatus ) were sampled using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Spectral analyses on these data estimate the timing of ingress at 68 days on average using strontium and 85 days using barium. Based on the inflection points of their nonlinear mixing curves, these data reveal entry and subsequent movement up-estuary. Moreover, we use these spectrally derived estimates to show that growth rates did not drive ingress timing for our samples. These data thus lend no support to the critical-size hypothesis in this instance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.030
GPT teacher head0.242
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2012
Admission routes1
Has abstractyes

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