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Record W4378906121 · doi:10.1016/j.crimmu.2023.100060

Semaphorins in immune cell function, inflammatory and infectious diseases

2023· review· en· W4378906121 on OpenAlexafffund
Rony Thomas, Xi Yang

Bibliographic record

VenueCurrent Research in Immunology · 2023
Typereview
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsSemaphorinImmune systemBiologyAngiogenesisImmunologyNervous systemPlexinInflammationFunction (biology)NeuroimmunologyNeuroscienceCell biologyReceptorCancer researchGenetics

Abstract

fetched live from OpenAlex

The Semaphorin family is a group of proteins studied broadly for their functions in nervous systems. They consist of eight subfamilies ubiquitously expressed in vertebrates, invertebrates, and viruses and exist in membrane-bound or secreted forms. Emerging evidence indicates the relevance of semaphorins outside the nervous system, including angiogenesis, cardiogenesis, osteoclastogenesis, tumour progression, and, more recently, the immune system. This review provides a broad overview of current knowledge on the role of semaphorins in the immune system, particularly its involvement in inflammatory and infectious diseases, including chlamydial infections.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.229
GPT teacher head0.439
Teacher spread0.210 · 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

Citations14
Published2023
Admission routes2
Has abstractyes

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