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<scp>LA</scp>‐<scp>ICP</scp>‐<scp>MS</scp> Analysis of Small Samples: Carbonate Reference Materials and Larval Fish Otoliths

2013· article· en· W2000070852 on OpenAlexaff
Angélique Lazartigues, Pascal Sirois, Dany Savard

Bibliographic record

VenueGeostandards and Geoanalytical Research · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNational Institutes of Natural Sciences
KeywordsCarbonateCalibrationOtolithAnalytical Chemistry (journal)PopulationLaser ablationFish <Actinopterygii>ChemistryMaterials scienceMineralogyLaserChromatographyOpticsBiologyFisheryPhysics

Abstract

fetched live from OpenAlex

This article proposes a methodology to analyse the composition of very small carbonate samples such as larval fish otoliths. The chemical composition of otoliths, which are carbonate structures in the inner ear, is often used to explore population dynamics in fishes. Recent advances in laser ablation‐inductively coupled plasma‐mass spectrometry have suggested its potential application to this field. In this study, analyses were performed using a 193 nm A r F Resonetics LA system, coupled to an Agilent 7700X‐ ICP ‐ MS , with the following ablation parameters: a beam diameter of 5 μm, energy of 3 mJ, 2.7 J cm −2 , laser repetition rate of 10 Hz and translation speed of 2.5 μm s −1 . NIST SRM 610 glass was used as the primary calibration material. Performing this protocol, characterisation of a USGS GP ‐4 reference material was achieved with suitable precision and accuracy, but the USGS MACS ‐3 reference material appeared more heterogeneous under the ablation conditions tested. Calibration was performed using two different beam diameters (5 and 11 μm). Capelin ( Mallotus villosus ) otoliths measuring between 10 and 20 μm in diameter were tested. Even though a smaller beam diameter and lower energy were used compared with those normally employed to analyse larger otoliths, the method was successful.

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)0.007
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient 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 score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.003
Science and technology studies0.0010.003
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.312
Teacher spread0.273 · 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

Citations37
Published2013
Admission routes1
Has abstractyes

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