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Record W2161145986 · doi:10.1577/t08-098.1

Lake Sturgeon Age Validation using Bomb Radiocarbon and Known‐Age Fish

2009· article· en· W2161145986 on OpenAlexaff
Ronald M. Bruch, Steven E. Campana, Shannon L. Davis‐Foust, Michael J. Hansen, John Janssen

Bibliographic record

VenueTransactions of the American Fisheries Society · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersWisconsin Department of Natural Resources
KeywordsLake sturgeonFish finOtolithSturgeonPectoral girdleAcipenserBiologySPINE (molecular biology)JuvenileFish <Actinopterygii>MeristicsAnatomyAge groupsFisheryZoologyEcologyDemography

Abstract

fetched live from OpenAlex

Abstract Pectoral fin spines have been the accepted structure for estimating the age of various sturgeon species for nearly 100 years, though other structures have also been used (otoliths, pectoral girdle, scutes, and caudal fulcra). Accuracy of age estimates using any of these structures has not been validated, so we report the first use of bomb radiocarbon ( 14 C) assays to assess the validity of ages estimated using growth increments on pectoral fin spine and otolith frontal cross sections from lake sturgeon Acipenser fulvescens ; we also assessed age estimates from pectoral fin spines of known‐age lake sturgeon. Growth increments on pectoral fin spine cross sections underestimated true age of fish older than 14 years and error increased with age, whereas otoliths accurately estimated true age up to at least 52 years. Increment formation on pectoral fin spine and otolith cross sections from juvenile lake sturgeon (ages 2‐11) was similar, although pectoral spines were clearer and easier to interpret. A power function (true age = [estimated age] 1.054796 , where estimated age was determined from pectoral spines; r 2 = 0.98) provides a means for correcting existing age estimates obtained from lake sturgeon pectoral fin spines.

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 score0.277
Threshold uncertainty score0.400

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)0.0000.000

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 head0.223
Teacher spread0.209 · 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

Citations107
Published2009
Admission routes1
Has abstractyes

Explore more

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