<scp>LA</scp>‐<scp>ICP</scp>‐<scp>MS</scp> Analysis of Small Samples: Carbonate Reference Materials and Larval Fish Otoliths
Bibliographic record
Abstract
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 ArF 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".