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Record W2852054128 · doi:10.1358/dot.2018.54.6.2856494

Life sciences licensing deals in the first quarter of 2018: updatesand trends

2018· article· en· W2852054128 on OpenAlex
E Cruces

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

VenueDrugs of today · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Value (mathematics)MedicineStatisticsMathematics

Abstract

fetched live from OpenAlex

During the first quarter of 2018, Cortellis Competitive Intelligence registered 879 new deals (excluding mergers and acquisitions) with a total disclosed deal value of approximately USD 35.2 billion as part of its ongoing coverage of licensing activity in the life sciences sector. This compares to 1,203 and USD 26.2 bil-lion in the fourth quarter of 2017, and 1,158 and USD 31.8 billion in the first quarter of 2017. This meant a significant increase in the total disclosed deal value compared to these two previous periods (+34% and +10.7%, respectively), and included the USD 5.8 billion pact between Merck and Co. and Eisai which became the highest-value deal in the last 4-year opening quarters. However, during the first quarter of 2018 there was not a high number of signed agreements versus the fourth quarter of 2017 and the first quarter of 2017 (-27% and -24%, respectively), reaching a number similar to that in the first quarter of 2014 with a total of 931 agreements covered.

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)0.010
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.066
GPT teacher head0.385
Teacher spread0.319 · 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