Validation of depth-profiling LA-ICP-MS in otolith applications
Bibliographic record
Abstract
Otolith microchemistry is a widely used technique for elucidating life history patterns in fishes. This typically involves sectioning the otolith and collecting elemental signatures via laser ablation. But this requires time-intensive handling that may influence results. As an alternative to traditional cut–polish–ablate techniques, we tested depth-profiling laser ablation, which offers reduced handling and contamination risk. To validate depth profiling as a robust method for collecting trace element otolith microchemistry data, we constructed composite otoliths using otolith materials from fishes of different origins (fresh water, seawater). Test ablations were conducted on composite diadromous otoliths at a range of spot sizes and pit depths. We measured tailing and fractionation effects in the following elements: Na, Mg, K, Mn, Zn, Rb, Sr, and Ba. Given appropriate instrument parameters, depth profiling can accurately collect elemental concentration data both between and within top and bottom layers of an otolith composite across a range of spot sizes and pit depths. Analytical power and lag effects were dependent on spot size, highlighting the importance of optimizing spot size based on sample morphology and instrument parameters.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".