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Record W2609292269 · doi:10.2527/asasann.2017.750

750 Animal models to study germ line stem cells and spermatogenesis

2017· article· en· W2609292269 on OpenAlexaff
Ina Dobrinski

Bibliographic record

VenueJournal of Animal Science · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsStem cellBiologyCell biologyAdult stem cellGermlineNicheStem cell theory of agingSpermatogenesisGerm cellProgenitor cellCellular differentiationStem cell factorGeneticsGeneEcologyEndocrinology

Abstract

fetched live from OpenAlex

Mammalian spermatogenesis is a stem cell-driven system. Germ line stem cells (spermatogonial stem cells) form the basis of male fertility and are the only cells in an adult body that divide and can contribute genes to subsequent generations, making them immediate targets for genetic manipulation. Stem cells have to maintain a delicate balance between self-renewal to maintain a functional stem cell pool and differentiation to sustain efficient, life-long production of sperm. However, relatively little is known about the mechanisms that govern this fate-decision. Similar to stem cells in other organs, germ line stem cells reside in a specialized microenvironment, the stem cell niche. Interactions between stem cells and their niche are essential for tissue homeostasis. Our work utilizes various mammalian animal models and transplantation technology to elucidate aspects of stem cell function, formation of the stem cell niche, and applications of stem cell-based technology to preservation of fertility and genetic modification of non-rodent animal models.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.006

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 head0.040
GPT teacher head0.302
Teacher spread0.262 · 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 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
Published2017
Admission routes1
Has abstractyes

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