Laser Diode Thermal Desorption‐Mass Spectrometry (LDTD‐MS)
Bibliographic record
Abstract
High-throughput analysis is a necessary concept to optimize the drug development process. LDTD-MS/MS is a powerful tool in this environment as it combines the sensitivity and speed required for this type of application. This chapter is intended to present several aspects of laser diode thermal desorption technology. The goal is for the reader to understand the technology, its advantages and limitations, the strategies to be used for their applications, and to make the best use of it. The chapter begins with a review of the instrumentation and the theory behind it. Then, the different approaches to sample preparation are presented to the reader. As the sample must be dried before being analyzed, the preparation differs from what is normally done in LC-MS/MS. The preparation techniques for LDTD-MS/MS analysis are adapted according to the matrices and applications. In order to give an overview of the field of application of the technology, several applications described in the literature are discussed. Finally, the advantages of this technique as well as its limitations are also discussed at the end of the chapter.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".