What proteomics has taught us about honey bee ( <i>Apis mellifera</i> ) health and disease
Bibliographic record
Abstract
The Western honey bee, Apis mellifera, is currently navigating a gauntlet of environmental pressures, including the persistent threat of parasites, pathogens, and climate change - all of which compromise the vitality of honey bee colonies. The repercussions of their declining health extend beyond the immediate concerns of apiarists, potentially imposing economic burdens on society through diminished agricultural productivity. Hence, there is an imperative to devise innovative monitoring techniques for assessing the health of honey bee populations. Proteomics, recognized for its proficiency in biomarker identification and protein-protein interactions, is poised to play a pivotal role in this regard. It offers a promising avenue for monitoring and enhancing the resilience of honey bee colonies, thereby contributing to the stability of global food supplies. This review delves into the recent proteomic studies of A. mellifera, highlighting specific proteins of interest and envisioning the potential of proteomics to improve sustainable beekeeping practices amidst the challenges of a changing planet.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".