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Breaking ground on translational stem cell research

2010· article· en· W2002836936 on OpenAlexfundno aff
Zach W. Hall, David J. Kahler, Michael Manganiello, Dieter Egli, Daylon James, Darja Marolt, Christopher A. Fasano, Justin K. Ichida, Scott Noggle, Susan Solomon, David McKeon, Kristin Smith, Caroline Marshall

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of HealthChina Scholarship CouncilUniversity of RochesterNew York Stem Cell FoundationMemorial Sloan-Kettering Cancer CenterHospital for Sick Children
KeywordsTranslational researchStem cellTranslational scienceFoundation (evidence)Library scienceMedicinePolitical scienceBiologyComputer sciencePathologyCell biologyLaw

Abstract

fetched live from OpenAlex

Sponsored by the New York Stem Cell Foundation (NYSCF), the "Fourth Annual Translational Stem Cell Research Conference: Breaking Ground" convened October 13-14, 2009 at The Rockefeller University in New York City to discuss translational stem cell research. Attracting over 400 scientists, patient advocates, and stem cell research supporters from fifteen countries, the two-day conference featured an afternoon of panel discussions, intended for a broad audience, followed by a second day of scientific talks and poster presentations. This report summarizes both days of this exciting conference.

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)0.120
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation 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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.120
Threshold uncertainty score0.634

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1200.094
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.002
Science and technology studies0.0060.028
Scholarly communication0.0200.031
Open science0.0030.018
Research integrity0.0200.060
Insufficient payload (model declined to judge)0.0180.006

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 head0.149
GPT teacher head0.398
Teacher spread0.249 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations5
Published2010
Admission routes1
Has abstractyes

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