Characterization of Heavily Contaminated Environments Using NMR Spectroscopy
Bibliographic record
Abstract
At present, NMR spectroscopy is used infrequently as an analytical tool during the environmental assessments of contaminated sites. Nevertheless, NMR exhibits many attributes that are complementary to the traditionally employed methods for environmental analysis and, as such, has the potential to provide important information that is often missed during standard environmental assessments. In general, conventional approaches for the characterization of contaminated environments are based on the identification and quantification of targeted contaminants of concern with the primary objective being to assess their levels relative to regulatory guidelines. NMR spectroscopy, in contrast, is useful as a tool for environmental analysis for its ability to provide insight into the type and extent of contamination present in a nontargeted manner, such that both suspected and unsuspected compounds may be identified. This article discusses the use of NMR spectroscopy in the environmental industry to improve our understanding of the distribution of chemical contaminants at sites that are undergoing remedial or monitoring activities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".