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Record W4377011598 · doi:10.1002/cyto.a.24739

OMIP‐92: Characterization of rat macrophage subsets, lymphocytes, and granulocytes in bronchoalveolar lavage fluid

2023· article· en· W4377011598 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCytometry Part A · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsBronchoalveolar lavageFlow cytometryImmunologyImmune systemInfiltration (HVAC)MacrophageLipopolysaccharideInflammationCytometryBiologyPathologyChemistryMedicineLungIn vitroInternal medicine

Abstract

fetched live from OpenAlex

Airway inflammation is a defense mechanism against inhaled agents characterized by infiltration of circulating immune cells. Given the inconsistent cellular identification across pre-clinical rat model, we have developed a flow cytometry panel of six colors to characterize macrophages subsets, lymphocytes and granulocytes in bronchoalveolar lavage fluid (BAL). Rats were challenged with intratracheal instillation of lipopolysaccharide (LPS). BAL were harvested 24 h after one LPS exposure in rats. This flow cytometry panel involve the description of macrophage subsets, T and B lymphocytes and neutrophils, which are central to airway immune responses, as based on scientific literature. By using a relatively small number of parameters to identify multiple cell types, additional parameters can be used for project/disease-specific activation markers.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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