Bibliographic record
Abstract
Agilent Technologies, a firm best known for selling scientific instruments, is making a splash in the drug services market. The California-based company has announced it will acquire the Canadian contract development and manufacturing organization (CDMO) BioVectra for $925 million. The investment will solidify Agilent’s position in making oligonucleotides, synthetic strands of DNA or RNA used in gene editing and in therapies that work by silencing or promoting the degradation of target RNA. Agilent already manufactures oligonucleotides to make guide RNAs (gRNAs) for six pharmaceutical companies, including Alnylam Pharmaceuticals and Novartis. But these gRNAs are just one part of the complex machinery used in CRISPR/Cas-9–based gene editing, says Brian Carothers, a vice president at Agilent. “With the BioVectra acquisition, we aim to be a one-stop shop for our clients who need the technology for gene editing.” Oligonucleotide demand has increased over the past 5 years as more pharmaceutical companies develop RNA-based
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.516 | 0.444 |
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".