Evaluating environmental, weather, and management influences for sustainable beekeeping in California and Quebec: Enhancing beehive survival predictions
Bibliographic record
Abstract
Concerned about declining managed honeybee populations in North America, this study employed the random survival forest (RSF) model to assess beehive mortality, considering 18 diverse environmental, weather, and management practices. Our analysis focused on 15,906 and 6,690 beehives in California state, United States, and Quebec province, Canada for 2023, respectively. The accuracy of the RSF model was assessed through three accuracy metrics, namely concordance index (C-index), integrated Brier score (IBS), and time-dependent area under the curve (AUC). Besides, the variables' importance was assessed in both California and Quebec under two criteria configurations, incorporating beehive management criteria and excluding such criteria. Including beehive-management criteria improved accuracy: for the test dataset in California, C-index of 0.8845, IBS of 0.0177, and time-dependent AUC of 0.8819; in Quebec, C-index of 0.9618, IBS of 0.0121, and time-dependent AUC of 0.9687. Comparing feature importance across the regions revealed differences, with sugar-feeding frequency, precipitation, and Miticide-treatment frequency exerting more influence in California and DEM, precipitation, and sugar-feeding frequency playing more substantial roles in Quebec. Beekeeping suitability maps for both regions were provided, classifying land suitability for beekeeping into five categories from very low to very high. By aggregating the areas classified as highly and very highly suitable for beekeeping, the beekeeping suitability maps indicated that 64.712% of California and 66.423% of Quebec exhibit suitable conditions for beekeeping. This study could contribute to reducing honeybee colony losses and supporting the advancement of sustainable agriculture in California and Quebec through 1) pinpointing essential environmental, ecological, and meteorological factors, alongside beehive management practices affecting beehive mortality, and 2) providing maps illustrating land suitability for beekeeping.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".