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Record W3114268994 · doi:10.1016/j.sjbs.2020.12.031

Fighting against the second wave of COVID-19: Can honeybee products help protect against the pandemic?

2020· review· en· W3114268994 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

VenueSaudi Journal of Biological Sciences · 2020
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)PropolisSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakHoney beeBee venomHoney BeesBeekeepingBiologyMedicineDiseaseTraditional medicineVirologyInfectious disease (medical specialty)EcologyZoology

Abstract

fetched live from OpenAlex

Coronavirus Disease (COVID-19) has infected people in 210 nations and has been declared a pandemic on March 12, 2020 by the World Health Organization (WHO). In the absence of effective treatment and/or vaccines for COVID-19, natural products of known therapeutic and antiviral activity could offer an inexpensive, effective option for managing the disease. Benefits of products of honey bees such as honey, propolis, and bee venom, against various types of diseases have been observed. Honey bees products are well known for their nutritional and medicinal values, they have been employed for ages for various therapeutic purposes. In this review, promising effects of various bee products against the emerging pandemic COVID-19 are discussed. Products of honey bees that contain mixtures of potentially active chemicals, possess unique properties that might help to protect, fight, and alleviate symptoms of COVID-19 infection.

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)0.005
metaresearch head score (Gemma)0.007
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 score0.995
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.003
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
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 head0.138
GPT teacher head0.301
Teacher spread0.164 · 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