Mass Spectrometry for Trace Analysis of Explosives in Water
Bibliographic record
Abstract
Harmful explosives can accumulate in natural waters over the long term during their testing, usage, storage, and dumping and can pose a health risk to humans and the environment. Mass spectrometry (MS) has become the most successful technology for the analysis of such compounds in water throughout the North America and Europe. During the past decade, improvements have occurred for MS analysis in understanding explosives ion behavior and the influence of additives and reagent gases on the formation of ions. Novel approaches also have been developed for fast MS analysis of explosives. These advances motivated an update from previous discussions of MS response to explosives. Recent findings collected from the past ten years have been assessed to provide a comprehensive view of the detection of explosives by MS. This review focuses on the most commonly used MS-based methods and newer developments for the MS analysis of explosives in water, with particular attention to mass identified ions and the performance of the methods. We also include results from comparative studies of explosives detection involving MS-methods that use various ionization techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".