Analysis of Soils and Sediments by Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS): An Innovative Tool for Environmental Forensics
Bibliographic record
Abstract
This article describes the applicability of a rapid laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) method for the analysis of soil and sediment samples with broad chemical and physical properties and the comparison of its analytical performance to digestion protocols commonly used in environmental sciences. Two sets of samples were evaluated, contaminated soils from a residential area and undisturbed sediments from natural environments. Analytical results obtained by LA-ICP-MS correlate well at the 95% confidence level to total digestion followed by inductively coupled plasma optical emission spectroscopy (ICP-OES) and ICP-MS analysis. A total of 48 sediments collected in South Florida were also analyzed for screening purposes and to evaluate the application of LA-ICP-MS in environmental forensics. Normalization using Al combined with non-parametric correlation tests and principal component analyses were successfully used to predict correlations between data acquired by LA-ICP-MS and by partial digestion methods followed by ICP methods. Precision and accuracy for the LA method was <20%, which is typically accepted for digestion methods of soils and sediments. The overall bias, evaluated on reference standard materials, ranged from 8%–15%. The overall precision obtained on samples was <10% relative standard deviation (RSD).
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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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".