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Record W2017733856 · doi:10.1155/2013/949513

Dendritic Cell Development: A Choose-Your-Own-Adventure Story

2013· article· en· W2017733856 on OpenAlexafffund
Amanda J. Moore, Michele K. Anderson

Bibliographic record

VenueAdvances in Hematology · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersUniversity of TorontoCanadian Institutes of Health ResearchSunnybrook Research Institute
KeywordsImmune systemDendritic cellCell biologyTranscription factorBone marrowLineage (genetic)SpleenImmunologyStem cellBiologyMedicineGeneGenetics

Abstract

fetched live from OpenAlex

Dendritic cells (DCs) are essential components of the immune system and contribute to immune responses by activating or tolerizing T cells. DCs comprise a heterogeneous mixture of subsets that are located throughout the body and possess distinct and specialized functions. Although numerous defined precursors from the bone marrow and spleen have been identified, emerging data in the field suggests many alternative routes of DC differentiation from precursors with multilineage potential. Here, we discuss how the combinatorial expression of transcription factors can promote one DC lineage over another as well as the integration of cytokine signaling in this process.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.007
GPT teacher head0.242
Teacher spread0.234 · 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 designBench or experimental
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

Citations17
Published2013
Admission routes2
Has abstractyes

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