Bibliographic record
Abstract
ARTICLES DISCUSSED: Hutchinson, A., Seitova, A. Production of recombinant PRMT proteins using the Baculovirus expression vector system. Journal of Visualized Experiments. (173), doi: 10.3791/62510 (2021). Maniaci, M., Marini, F., Massignani, E., Bonaldi, T. A mass spectrometry-based proteomics approach for global and high-confidence protein R-Methylation analysis. Journal of Visualized Experiments. (182), doi: 10.3791/62409 (2022). Habisch, H., Zhang, F., Zhou, Q., Madl, T. Exploring the arginine methylome by nuclear magnetic resonance spectroscopy. Journal of Visualized Experiments. (178), doi: 10.3791/63245 (2021). Szewczyk, M. M., Vu, V., Barsyte-Lovejoy, D. Quantitative methods to study protein arginine methyltransferase 1-9 activity in cells. Journal of Visualized Experiments. (174), doi: 10.3791/62418 (2021). Poulard, C., Jacquemetton, J., Valantin, J., Treilleux, I., Le Romancer, M. Proximity ligation assay allows the detection, localization, and quantification of protein arginine methylation in fixed tissue. Journal of Visualized Experiments. (185), doi: 10.3791/64294 (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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".