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Record W3009918887 · doi:10.1387/ijdb.190362id

Brinster Spermatogonial Stem Cell Transplantation 25th Anniversary Symposium

2019· article· en· W3009918887 on OpenAlexaff
Ina Dobrinski, Kyle E. Orwig

Bibliographic record

VenueThe International Journal of Developmental Biology · 2019
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTransplantationBiologyStem cell biologyLibrary scienceStem cellPhysiologyEnvironmental ethicsReproductive technologyMedicineCell biologyInternal medicineComputer scienceEmbryo

Abstract

fetched live from OpenAlex

ABSTRACT The Symposium, co-sponsored by the Institute of Regenerative Medicine, the University Research Foundation, the Center for Research on Reproduction and Women’s Health, the Penn Center for the Study of Epigenetics in Reproduction, and Penn Vet at the University of Pennsylvania, commemorated the 25 th anniversary of the first publications describing spermatogonial stem cell (SSC) transplantation in mice. This transformative approach has propelled advances in our understanding of germ cell biology, has been translated to a variety of vertebrate species, and holds translational potential for fertility restoration in patients. The symposium opened with a lecture by Dr. Brinster reflecting on the origin of the work, as well as advances over the 25 years up to present ongoing studies. Following Dr. Brinster’s remarks, 10 lectures were presented by distinguished scientists, including several of Dr. Brinster’s former trainees and colleagues. The symposium closed with a keynote lecture by Dr. David Page. Topics ranged from aspects of basic SSC biology to applications in large animal models and potential translation to treating human male infertility. Many of the studies presented directly resulted from SSC transplantation technology highlighting its tremendous impact in advancing the field. The Symposium program and the lectures can be found at https://spark.adobe.com/page/jS0cDLzLHvOiJ

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.306

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 integrity0.0000.000
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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2019
Admission routes1
Has abstractyes

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