Histology of the whole body of honey bees: tissue fixation and processing
Bibliographic record
Abstract
Beekeeping plays a crucial role in biodiversity, pollination, commercial farming, and the worldwide agricultural economy. Histopathology, which is an important tool for the investigation of diseases in vertebrates, is not commonly used in honey bees ( Apis mellifera). However, histopathology could potentially help the diagnostic investigation of high mortality in bees. We developed a tissue fixation and processing method enabling systematic production of histologic slides adequate for diagnostic and research purposes. Our method uses inexpensive, accessible products and can be realized with conventional pathology laboratory equipment. The quality of histologic slides obtained is similar to those of vertebrate animals processed routinely in pathology laboratories. Histopathology as a diagnostic and research tool will improve the services currently offered to apiarists and could help decrease the mean mortality rate, increase apiarists’ profits, and ensure long-term pollination services.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".