A Full Scan Data Review Tool to Match the Speed of Acoustic Ejection Mass Spectrometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Acoustic ejection mass spectrometry (AEMS) has recently emerged as the premier ultrahigh-throughput mass spectrometric methodology for drug discovery and related fields. The ultrahigh analytical speed (~1 s/sample) of AEMS has significantly enhanced the efficiency of many high throughput applications. As a result, a data processing and reviewing tool with a matching speed is in high demand for the large amount of data generated, especially for applications such as quality control (QC) of compound collections and high throughput chemistry, where full-scan MS data required convoluted subsequent peak extraction and evaluation. In this study, we demonstrated the feasibility of a tool developed specifically for this purpose. The process using the tool involved automated splitting of the full scan data to correlate well positions with each signal peak, extraction of expected mass traces, and subsequent peak integration. Data evaluation based on verification rules, such as detected mass accuracy, isotopic pattern, and signal-to-noise ratio (S/N), enabled a comprehensive assessment of sample quality that was complemented by visualization in the form of a plate heat map generated from the selected rules. The tool demonstrated fast and straightforward data review and reporting and, more importantly, at a matching speed of sample analysis by acoustic ejection mass spectrometry. The choice of data processing and storage over the cloud further facilitated results sharing among data users.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.000 |
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 it