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Record W2017223686 · doi:10.2527/jas.2008-1427

BOARD-INVITED REVIEW: The biology and regulation of preadipocytes and adipocytes in meat animals1,2

2008· review· en· W2017223686 on OpenAlexaff
Gary Hausman, Michael V. Dodson, Kolapo M. Ajuwon, M. J. Azain, Kimberly M Barnes, Le Luo Guan, Zhihua Jiang, Sylvia P. Poulos, R. D. Sainz, Stephen B. Smith, Michael E. Spurlock, Jan Novakofski, Melinda E. Fernyhough, Werner G. Bergen

Bibliographic record

VenueJournal of Animal Science · 2008
Typereview
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMarbled meatAdipose tissueAdipocyteFood scienceBiologyEndocrinologyInternal medicineCell biologyAnimal scienceMedicine

Abstract

fetched live from OpenAlex

The quality and value of the carcass in domestic meat animals are reflected in its protein and fat content. Preadipocytes and adipocytes are important in establishing the overall fatness of a carcass, as well as being the main contributors to the marbling component needed for consumer preference of meat products. Although some fat accumulation is essential, any excess fat that is deposited into adipose depots other than the marbling fraction is energetically unfavorable and reduces efficiency of production. Hence, this review is focused on current knowledge about the biology and regulation of the important cells of adipose tissue: preadipocytes and adipocytes.

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)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.008

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 head0.036
GPT teacher head0.347
Teacher spread0.311 · 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
GenreReview

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

Citations354
Published2008
Admission routes1
Has abstractyes

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