Nuclear Magnetic Resonance Spectroscopy for the Detection and Quantification of Abused Drugs
Bibliographic record
Abstract
Abstract Nuclear magnetic resonance (NMR) spectroscopy provides the forensic analyst with an extremely powerful tool for the detection and quantification of abused drugs. A whole range of one‐dimensional (1‐D) and two‐dimensional (2‐D) NMR techniques is available for performing the required analyses. These NMR methods may be used for routine purposes, such as to confirm the identity of a drug or quantify the amount of illicit substance present in a police exhibit. However, the area where NMR stands out as an analytical tool is in the identification of unknown compounds, such as “designer drugs”. NMR is also used in police intelligence work, as it can provide clues to the synthetic route used to prepare the drug. This is done by impurity profiling or by determining the drug's optical purity. Although NMR has been used for many years to analyze abused drugs, even the most modern spectrometers lack the sensitivity obtainable by other techniques such as mass spectrometry (MS) or high‐performance liquid chromatography (HPLC). However, NMR is a nondestructive technique which provides essential structural information which cannot be obtained from these other methods. NMR also has the distinct advantage of not requiring reference standards for the identification of unknowns.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.027 | 0.019 |
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".