Early prediction of honeybee hive winter survivability using multi-modal sensor data
Bibliographic record
Abstract
Winter mortality is one of the main causes of beehive loss. However, very limited tools can be used by beekeepers to identify the high-risk colonies at an early stage. In this study, we propose a multi-modal sensor (audio, humidity, temperature) based system to predict the beehive winter survivability. More specifically, we first propose a multi-modal feature set, which is shown to be highly correlated with winter survival rate, and develop a machine learning model to further detect the hives that are less likely to survive the winter. Our top-performing model achieves an AUC-ROC score of 0.730 based on one-year-long data collected from 45 hives located in two different apiaries in Canada. Our findings show the feasibility of capturing high-risk hives at the early stage using multi-modal sensor data. Furthermore, we highlight the importance of bee audio in measuring survivability over other more widely-used modalities. Future study will focus on improving the generalizability of the prediction model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".