The spatial resolution of LA-ICP-MS line scans across heterogeneous materials such as fish otoliths and zoned minerals
Bibliographic record
Abstract
LA-ICP-MS line scans can provide equivalent or better information about the distribution of elements in heterogeneous solids than discrete spot analysis; and at much reduced time and cost. However, to do so, the spatial resolution for given instrumentation and operating conditions must be known. Here, we present a quantitative and reproducible method to do this that requires line scans across a sandwich of three glasses of varying certified concentrations of trace elements. To produce sufficient counting statistics, only Ca, Sr, Rb, and Pb were analysed. Raw data (counts per second) are reduced to “instantaneous concentration” and then filtered to produce concentration profiles that contain the same dimensions as the original data. The spatial resolution is empirically determined from these profiles by using a statistical “confidence” window. Spatial resolution is controlled by the magnitude of concentration gradients, the direction of concentration shifts, and the instrumental configuration and settings such as cell size and shape, and the speed of the scan. Spatial resolution is better for increasing concentration profiles than decreasing ones by a factor of 2. The relationship between the magnitude of the concentration gradient and the spatial resolution is linear. Therefore, once the range of concentration variation is known in any target, a minimum resolution can be determined from this linear relationship. The spatial resolutions of the three elements examined are observed to be the same suggesting that element specific ablation behaviours are not a significant control on spatial resolution. A Sr concentration profile from a natural sample (fish otolith) is generated, and the resolutions from the glass sandwich are applied. For the concentrations observed and the settings and hardware used, a minimum spatial resolution of 50 µm was calculated. Concentration variations at smaller scales can be detected but not quantified.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".