Inductively Coupled Plasma Mass Spectrometry Analysis of Environmental Samples for the Quantification of Potentially Toxic Species
Bibliographic record
Abstract
Abstract The analysis of environmental samples is widely carried out to determine if a soil is safe for agricultural use, if water is safe to drink, if the air that we breathe can make us sick, how far reaching is pollution, if a contaminated site has been suitably remediated, etc. This article discusses how inductively coupled plasma mass spectrometry (ICPMS) can be used to this end. Indeed, with detection limits at the parts per trillion or even parts per quadrillion level, depending on the element and the instrument, ICPMS is very advantageous for the detection and quantification of toxic or potentially toxic species in environmental samples. This article describes sample preparation for ICPMS analysis and instrument calibration to yield accurate quantitative results. This includes the precautions that are often required, which vary depending on the type of analysis, sample matrix, instrument, and elements to be determined. It also includes techniques that can be coupled to ICPMS to increase its capabilities, such as laser ablation or electrothermal vaporization for direct solid analysis and liquid chromatography, gas chromatography, or capillary electrophoresis for speciation analysis, as speciation information is lost in the inductively coupled plasma (ICP) where all compounds are atomized.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".