Current honey bee stressor investigations and mitigation methods in the United States and Canada
Bibliographic record
Abstract
Honey bees are the most important managed insect pollinators in the US and Canadian crop systems. However, the annual mortality of colonies in the past 15 years has been consistently higher than historical records. Because they are eusocial generalist pollinators and amenable to management, honey bees provide a unique opportunity to investigate a wide range of questions at molecular, organismal, and ecological scales. Here, the American Association of Professional Apiculturists (AAPA) and the Canadian Association of Professional Apiculturists (CAPA) created 2 collections of articles featuring investigations on micro and macro aspects of honey bee health, sociobiology, and management showcasing new applied research from diverse groups studying honey bees (Apis mellifera) in the United States and Canada. Research presented in this special issue includes examinations of abiotic and biotic stressors of honey bees, and evaluations and introductions of various stress mitigation measures that may be valuable to both scientists and the beekeeping community. These investigations from throughout the United States and Canada showcase the wide breadth of current work done and point out areas that need further research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".