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Contemplating the murine test tube: lessons from natural killer cells and<i>Cryptococcus neoformans</i>

2006· review· en· W2069645959 on OpenAlexaff
Kaleb J. Marr, Gareth J. Jones, Christopher H. Mody

Bibliographic record

VenueFEMS Yeast Research · 2006
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCryptococcus neoformansBiologyImmune systemEffectorRepertoireImmunityImmunologyLaboratory mouseCryptococcosisNatural killer cellGeneMicrobiologyGeneticsCytotoxic T cell

Abstract

fetched live from OpenAlex

Murine experimentation has provided many useful tools, including the ability to knockout or over-express genes and to perform experiments that are limited by ethical considerations. Over the past century, mice have imparted valuable insights into the biology of many systems, including human immunity. However, although there are many similarities between the immune response of humans and mice, there are also many differences; none is more prominent than when examining natural killer cell biology. These differences include tissue distribution, effector molecules, receptor repertoire, and cytokine responses, all of which have important implications when extrapolating the studies to the human immune responses to Cryptococcus neoformans.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.676
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.004

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 head0.077
GPT teacher head0.369
Teacher spread0.292 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2006
Admission routes1
Has abstractyes

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