Modelling the Effects of Stressors and Treatment in a Honeybee Colony
Bibliographic record
Abstract
Honeybees are agriculturally important through their pollination work and production of honey. They are also vulnerable to the accumulation of stressors, which cause drastic colony losses. It is important to understand the effect of stress on a hive so that the cost of testing, treatment, and rest can be evaluated. We have developed a model of honeybee stress and simulated scenarios of testing and treatment regiments. The model tracks importation of stressors through foraging, and quantifies their effect on bee stress. The model is used to determine appropriate times for testing and treatment before, during, and after pollination jobs, and it is used to determine resting periods needed between pollination jobs, depending on testing and treatment use, to minimize the probability of bee loss and maximize profit for a beekeeper. Ultimately, the model will be used to inform testing and treatment strategies that will increase economic profitability for the beekeeping industry, and the agricultural sector as a whole.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".