A new retention index system for liquid chromatography-mass spectrometry
Bibliographic record
Abstract
Liquid chromatography with mass spectrometry (LC-MS) or UV absorbance (LC-UV) detection is used for the analysis of a wide range of compounds. Retention times, along with mass or UV spectra, are essential for identification of analytes in complex samples. Unfortunately, absolute retention times can be highly variable between different laboratories and instruments, and even between days in the same laboratory. This usually requires the analysis of chemical reference standards with each batch of samples to allow a good match of retention times for conclusive identification. This can be particularly important when dealing with isomers, which are often not well distinguished by mass spectra. Development of modern LC-MS methods based on techniques like scheduled selected reaction monitoring also requires the availability of standards to set retention windows. Not every laboratory can stock all standards as analysts may be concerned with the monitoring for hundreds of possible analytes. It would be helpful to have a better way of cataloging retention data so that analytes can be more easily identified through a match of retention times without the use of in-house standards. A better way to report retention data is to use “retention index (RI)” values. In this procedure, a series of homologous reference compounds are co-injected with the analytes.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.025 |
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".