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Record W2168509611 · doi:10.1016/s0008-6363(03)00530-3

Of mice and men, rats and atherosclerosis

2003· letter· en· W2168509611 on OpenAlexaff
James B. Russell

Bibliographic record

VenueCardiovascular Research · 2003
Typeletter
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhysiologyBiologyCognitive scienceMedicinePsychology

Abstract

fetched live from OpenAlex

See article by Lyngdorf et al. [10] (pages 854–862) in this issue. In less than 100 years, biology and medicine have been transformed by the effects of research that has eclipsed that of the 19th century in chemistry and that of the early half of the 20th century in physics. Whereas clinical research on human subjects has been important at the level of the application of knowledge at the bedside, the fundamental discoveries have been the result of basic science. Since the days of Harvey [1], real advances in the biomedical sciences have depended critically on the use of animals as models of human physiology, pathophysiology, and metabolism. Current animal models constitute technology that has been derived from scientific advances that, in turn, foster new scientific understanding and developments. The widespread use of rodent models dates from seminal work at the Wistar Institute where, starting in 1906, Donaldson [2] established the rat as a defined animal model for the study of many aspects of physiology. As he pointed out, the rat has many similarities to humans and its rapid development makes the study of life cycle processes practical. It is also both small enough to be inexpensive to breed and house and large enough to allow many studies of physiology and metabolism that are difficult or impossible in smaller species. The development of the mouse came somewhat later, led by work at the Jackson Laboratory, and benefited greatly from new techniques in genetics. The unraveling of the genetic code … *Tel.: +1-780-492-6359; fax: +1-780-492-1308.

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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.306
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.089
GPT teacher head0.336
Teacher spread0.247 · 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 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

Citations18
Published2003
Admission routes1
Has abstractyes

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