Individual and social defenses in Apis mellifera: a playground to fight against synergistic stressor interactions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".