Bibliographic record
Abstract
ARTICLES DISCUSSED: Kovalchuk, S. I., Ziganshin, R., Shelukhina, I. Simple in-house ultra-high performance capillary column manufacturing with the FlashPack approach. Journal of Visualized Experiments. (178), e62522 (2021). Sirois, I., Isabelle, M., Duquette, J. D., Saab, F., Caron, E. Immunopeptidomics: Isolation of mouse and human MHC Class I- and II-associated peptides for mass spectrometry analysis. Journal of Visualized Experiments. (176), e63052 (2021). Han, Y., Thomas, C. T., Wennersten, S. A., Lau, E., Lam, M. P. Y. Shotgun proteomics sample processing automated by an open-source lab robot. Journal of Visualized Experiments. (176), e63092 (2021). Nickerson, J. L. et al. Organic solvent-based protein precipitation for robust proteome purification ahead of mass spectrometry. Journal of Visualized Experiments. (180), e63503 (2022). Li, D., Liang, J., Zhang, Y., Zhang, G. An integrated workflow of identification and quantification on FDR control-based untargeted metabolome. Journal of Visualized Experiments. doi: 10.3791/63625-v (2022). Petelski, A. A., Nikolai Slavov, N., Specht, N. Single-cell proteomics preparation for mass spectrometry analysis using freeze-heat lysis and an isobaric carrier. Journal of Visualized Experiments. doi: 10.3791/63802 (2022).
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.012 |
| Insufficient payload (model declined to judge) | 0.068 | 0.133 |
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".