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Record W2321842580 · doi:10.1021/cen-v088n006.p011

MORE R&D CUTS FROM BIG PHARMA

2010· article· en· W2321842580 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueChemical & Engineering News · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsnot available
FundersVrije Universiteit BrusselAstraZenecaPfizer
KeywordsBusinessEconomicsChemistryPhysics

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.007
GPT teacher head0.203
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it