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Record W3125543266

Pluripotent Patents Make Prime Time: An Analysis of the Emerging Landscape

2010· article· en· W3125543266 on OpenAlexaff
Brenda M. Simon, Charles E. Murdoch, Christopher Thomas Scott

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInduced pluripotent stem cellScope (computer science)PaceStem cellEmbryonic stem cellBusinessBiologyGeographyComputer scienceCell biologyGenetics
DOInot available

Abstract

fetched live from OpenAlex

This paper explores the emerging landscape of patents related to induced pluripotent stem cells. These stem cells do not raise the same ethical issues as human embryonic stem cells, as they do not require the use of human embryos.When induced pluripotent stem cells burst onto the scene in 2007, they brought along with them a new approach to stem cell research. The field has moved along at a blistering pace, and this is reflected in the international patent landscape. Dozens of applications have been filed internationally, and in the past two years, the first three patents including claims to this technology have issued in Japan, the United Kingdom, and the United States. In our paper, we briefly examine the potential scope of the issued patents.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.008
Science and technology studies0.0030.005
Scholarly communication0.0110.012
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.006
GPT teacher head0.258
Teacher spread0.252 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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