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Record W4383556494 · doi:10.3389/fphys.2023.1172859

Individual and social defenses in Apis mellifera: a playground to fight against synergistic stressor interactions

2023· review· en· W4383556494 on OpenAlexafffund
Joy Gaubert, Pierre Giovenazzo, Nicolas Derôme

Bibliographic record

VenueFrontiers in Physiology · 2023
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsUniversité Laval
FundersMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
KeywordsStressorBiologyAbiotic componentDefence mechanismsEcologyFight-or-flight responseNeuroscience

Abstract

fetched live from OpenAlex

The honeybee is an important species for the agri-food and pharmaceutical industries through bee products and crop pollination services. However, honeybee health is a major concern, because beekeepers in many countries are experiencing significant colony losses. This phenomenon has been linked to the exposure of bees to multiple stresses in their environment. Indeed, several biotic and abiotic stressors interact with bees in a synergistic or antagonistic way. Synergistic stressors often act through a disruption of their defense systems (immune response or detoxification). Antagonistic interactions are most often caused by interactions between biotic stressors or disruptive activation of bee defenses. Honeybees have developed behavioral defense strategies and produce antimicrobial compounds to prevent exposure to various pathogens and chemicals. Expanding our knowledge about these processes could be used to develop strategies to shield bees from exposure. This review aims to describe current knowledge about the exposure of honeybees to multiple stresses and the defense mechanisms they have developed to protect themselves. The effect of multi-stress exposure is mainly due to a disruption of the immune response, detoxification, or an excessive defense response by the bee itself. In addition, bees have developed defenses against stressors, some behavioral, others involving the production of antimicrobials, or exploiting beneficial external factors.

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 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 score0.980
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.096
GPT teacher head0.341
Teacher spread0.244 · 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 teacher head, 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

Citations13
Published2023
Admission routes2
Has abstractyes

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