Bibliographic record
Abstract
A robust platform for facile defined glycan synthesis does not exist. Yet the need for such technology has never been greater as researchers seek to understand the full scope of carbohydrate function, stretching beyond the classical roles of structure and energy storage to encompass highly nuanced cell signaling events. To comprehensively explore and exploit the full diversity of carbohydrate functions, we must first be able to synthesize them in a controlled manner. Toward this goal, traditional chemical syntheses are inefficient while nature's own synthetic enzymes, the glycosyl transferases, can be challenging to express and expensive to employ on scale. Glycoside hydrolases represent a pool of glycan processing enzymes that can be either used in a transglycosylation mode or, better, engineered to function as "glycosynthases," mutant enzymes capable of assembling glycosides. Glycosynthases grant access to valuable glycans that act as functional and structural probes or indeed as inhibitors and therapeutics in their own right. The remodelling of glycosylation patterns in therapeutic proteins via glycoside hydrolases and their mutants is an exciting frontier in both basic research and industrial scale processes.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".