ProtectaBEE® and bee vectoring: innovating hive-based health with inspensing technology for sustainable apiculture
Bibliographic record
Abstract
Apivectoring, or bee vectoring, employs managed bees to distribute powders containing disease and pest-fighting biocontrol agents during pollination flights to crops. Our research introduces a novel application of this concept, termed inspensing, which leverages bee vectoring for hive-based benefits. In inspensing, bees traverse through a carrier powder combined with products aimed at combating pathogens or pests within the hive. To facilitate this, we developed the ProtectaBEE® system, an innovative beehive-entrance technology that guides bees through a compartment inoculated with an inspensing powder. This system facilitates the application of beneficial agents into the hive without the need for beekeepers to open the hive, thereby streamlining the treatment process and reducing hive disturbance. To analyze the effectiveness of the system, we employed a fluorescent tracer in a powder formulation for tracking distribution throughout the hive. Complementing this, we inspensed a living dry powder-formulated biocontrol agent, Beauveria bassiana, an entomopathogenic fungus known to reduce Varroa mite populations, and detected its presence in the hive using PCR. The fluorescent powder was detected in 78.8% of the samples while B. bassiana was confirmed in up to 86.2% of larvae and 91.7% of mites. Our results underscore the system's efficacy in delivering material throughout the hive and affirm the potential for inspensing dry-powder-formulated biocontrol agents to manage Varroa destructor. Inspensing paves new paths for optimizing bee health and pest control strategies, streamlining disease management, simplifying hive maintenance, and minimizing beekeeper intervention, all contributing to sustainable apiculture.
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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".