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Record W2936450916 · doi:10.1071/mf18354

Bomb radiocarbon age validation for the long-lived, unexploited Arctic fish species Coregonus clupeaformis

2019· article· en· W2936450916 on OpenAlexaff
John M. Casselman, Cynthia M. Jones, Steven E. Campana

Bibliographic record

VenueMarine and Freshwater Research · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsQueen's University
FundersDivision of Ocean Sciences
KeywordsCoregonus clupeaformisArcticRadiocarbon datingOtolithLongevityTemperate climatePopulationFisheryCoregonusEcologyBiologyFish <Actinopterygii>DemographyPaleontology

Abstract

fetched live from OpenAlex

The growth rates of freshwater fish in the Arctic would be expected to be very low, but some previous studies of lake whitefish (Coregonus clupeaformis) have reported relatively rapid growth and longevity estimates of less than 15 years. We used bomb radiocarbon chronologies to validate an ageing method based on otolith sections for lake whitefish in both an unexploited Arctic lake (MacAlpine Lake; longevity 50 years) and a lightly exploited temperate population (Lake Simcoe; longevity 49 years). Our results confirm previous suggestions that other ageing methods can seriously underestimate lake whitefish age after ~5–8 years. A Chapman–Robson estimate of instantaneous natural mortality rate (M) of 0.12 in the unfished Arctic lake was one-quarter of that measured in other Arctic lake whitefish populations, and one-third of that predicted by Pauly’s (1980) growth–temperature equation. The high estimates of M reported in other whitefish studies and by Pauly’s equation are almost certainly due to their being based on (incorrect) scale or surface otolith ages. Radiocarbon dating confirms that any attempt at predicting sustainable production for long-lived freshwater fishes like lake whitefish will need to be based on accurate ages derived from otolith sections.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.000

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 head0.060
GPT teacher head0.293
Teacher spread0.232 · 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

Citations24
Published2019
Admission routes1
Has abstractyes

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