Standard methods for maintaining adult<i>Apis mellifera</i>in cages under<i>in vitro</i>laboratory conditions
Bibliographic record
Abstract
SummaryAdult honey bees are maintained in vitro in laboratory cages for a variety of purposes. For example, researchers may wish to perform experiments on honey bees caged individually or in groups to study aspects of parasitology, toxicology, or physiology under highly controlled conditions, or they may cage whole frames to obtain newly emerged workers of known age cohorts. Regardless of purpose, researchers must manage a number of variables, ranging from selection of study subjects (e.g. honey bee subspecies) to experimental environment (e.g. temperature and relative humidity). Although decisions made by researchers may not necessarily jeopardize the scientific rigour of an experiment, they may profoundly affect results, and may make comparisons with similar, but independent, studies difficult. Focusing primarily on workers, we provide recommendations for maintaining adults under in vitro laboratory conditions, whilst acknowledging gaps in our understanding that require further attention. We specifically describe how to properly obtain honey bees, and how to choose appropriate cages, incubator conditions, and food to obtain biologically relevant and comparable experimental results. Additionally, we provide broad recommendations for experimental design and statistical analyses of data that arises from experiments using caged honey bees. The ultimate goal of this, and of all COLOSS BEEBOOK papers, is not to stifle science with restrictions, but rather to provide researchers with the appropriate tools to generate comparable data that will build upon our current understanding of honey bees.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.039 |
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".