Validation of Sr isotopes in otoliths by laser ablation multicollector inductively coupled plasma mass spectrometry (LA-MC-ICPMS): opening avenues in fisheries science applications
Bibliographic record
Abstract
Advances in probe-based mass spectrometry allow for high spatial resolution of elemental and isotopic signatures in fish otoliths that can be used to address fundamental questions in fisheries ecology. Analyses of Chinook salmon (Oncorhynchus tshawytscha) otoliths from two river populations yield identical 87Sr/86Sr ratios using laser ablation multicollector inductively coupled plasma mass spectrometry (LA-MC-ICPMS) and thermal ionization mass spectrometry (TIMS). Results were obtained from freshwater otoliths with low Sr concentrations (300800 ppm) using high spatial resolution (50 µm) corresponding to temporal histories of ~12 days fish growth. Low natural variation in 87Sr/86Sr among otoliths from the same rivers allows for conservative estimates of external precision of techniques. Thus, we demonstrate that Sr isotope ratios obtained by LA-MC-ICPMS can be accurate and precise, bypassing the time-intensive sample preparation required by microdrilling and TIMS. This technique opens the use of Sr isotopes for broader ecological questions requiring large sample sizes to characterize nursery habitats, metapopulation dynamics, and stock discrimination similar to studies that focus on elemental concentrations, thereby providing a more robust tool for some freshwater and diadromous fishes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".