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Record W2477295508 · doi:10.1111/mms.12347

Use of mass spectrometry to measure aspartic acid racemization for ageing beluga whales

2016· article· en· W2477295508 on OpenAlexafffundabout
Kerri Pleskach, William Hoang, Mitchell Chu, Thor Halldorson, Lisa L. Loseto, Steven H. Ferguson, Gregg T. Tomy

Bibliographic record

VenueMarine Mammal Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of ManitobaFisheries and Oceans Canada
FundersFisheries and Oceans CanadaFisheries Joint Management Committee
KeywordsBelugaChromatographyChemistryRacemizationRepeatabilityDetection limitMass spectrometryDerivatizationBeluga WhaleLens (geology)Analytical Chemistry (journal)ArcticBiologyFisheryStereochemistryEcology

Abstract

fetched live from OpenAlex

Abstract We developed a novel analytical method to measure the D‐ and L‐isomers of aspartic acid ( AA ) in the eye lens of beluga whales ( Delphinapterus leucas ) for age determination. The method was based on hydrolysis of the eye lens under acidic conditions followed by direct injection onto a Chirobiotic T (25 cm × 4.6 ID , 5 μm particle size) high performance liquid chromatography analytical column and detection by tandem mass spectrometry operated in the negative ionization mode. The detection limit of the method was 550 pg for each isomer, the repeatability expressed as the relative standard deviation was 8% and the linear dynamic range was from 0.05 mM to 1 mM . The validated method was used to estimate, for the first time, the rate of racemization ( K asp ) of the two AA isomers and also the ratio of D/L at age 0, (D/L) 0 , in 34 beluga whales from the Canadian Arctic. At a mean ocular lens temperature of 17.8°C, respective K asp and (D/L) 0 were 3.48 ± 1.47 × 10 −3 /yr and 0.010 ± 0.005. We evaluated factors that impact K asp and affect uncertainty in age estimation and outline the steps required to incorporate the method in wildlife management decisions.

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)0.001
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 score0.478
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.037
GPT teacher head0.247
Teacher spread0.211 · 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

Citations12
Published2016
Admission routes3
Has abstractyes

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