Bibliographic record
Abstract
SEEKING TO IMPROVE productivity and offset the impact of generic competition, major drug companies GlaxoSmithKline, AstraZeneca, and Pfizer are making more cuts to their internal research operations. In announcing earnings last week, GSK said it wants to carve $800 million out of its cost structure by 2012; half of that amount will come from R&D. Though the company isn’t specifying how many jobs will be cut, it does say the bulk of the savings will come from a “reduction of infrastructure.” GSK has proposed ending R&D activities across several sites, including Tonbridge, U.K., which is expected to be closed; Verona, Italy; Zagreb, Croatia; and Ponzan, Poland, a company spokesperson confirms. Further, the company has proposed ending preclinical development at its Mississauga, Ontario, site, and end neurosciences drug activity in Harlow, U.K. In addition, GSK is abandoning research in select neuroscience areas, including depression and pain. At the same time, it has created a new ...
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.089 | 0.050 |
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".