Bquant – Novel script for batch quantification of LCMS data
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
Quantitative target analysis by liquid chromatography coupled to mass spectrometry (LCMS) is ubiquitous in environmental, metabolomic and toxicological studies. Targeted LCMS methods are capable of the simultaneous determination of literally hundreds of analytes. Although acquiring of instrumental data is very fast, data post-processing i.e. quantification can be time consuming step (and)or dependent to various commercial software packages. In attempt to facilitate this drawback Wolfram Mathematica script for batch quantification of LCMS data was created. Script works with direct outputs of integration algorithms created by different instrument control software's or custom created outputs. Key benefits of Bquant script are: •simple and automated routine for batch mode quantification•vast improvement in processing time (especially compared to manual interpretation)•data can be quickly re-analysed using different inputs Script was validated on various datasets and some of these were provided as working examples.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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