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Record W3104468022 · doi:10.3390/molecules25225378

Development of Fish Immunity and the Role of β-Glucan in Immune Responses

2020· review· en· W3104468022 on OpenAlex
Marianna Vaz Rodrigues, Fábio S. Zanuzzo, João Fernando Albers Koch, Carlos Alberto Ferreira de Oliveira, Petr Šíma, Václav Větvička

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

VenueMolecules · 2020
Typereview
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsImmune systemImmunityBiologyFish <Actinopterygii>GlucanAquacultureImmunologyFisheryBiochemistry

Abstract

fetched live from OpenAlex

Administration of β-glucans through various routes, including immersion, dietary inclusion, or injection, have been found to stimulate various facets of immune responses, such as resistance to infections and resistance to environmental stress. β-Glucans used as an immunomodulatory food supplement have been found beneficial in eliciting immunity in commercial aquaculture. Despite extensive research involving more than 3000 published studies, knowledge of the receptors involved in recognition of β-glucans, their downstream signaling, and overall mechanisms of action is still lacking. The aim of this review is to summarize and discuss what is currently known about of the use of β-glucans in fish.

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 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 score0.993
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

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

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 head0.015
GPT teacher head0.257
Teacher spread0.242 · 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