Managing Apis Mellifera Bees’ Mortality to Protect the Environmental Sustainability: Perceptions, Practices and Solutions of Beekeepers
Bibliographic record
Abstract
This study observes the managerial practices and perceptions of beekeepers during the decline of bees, in a context of high and increasing bee mortality that approaches 30% per year in Europe. Data collection was done through questionnaires and interviews with French beekeepers between 2018 and 2019. We analyze the results under the prism of stakeholder theory to identify all stakeholders (veterinarians, trainers, farmers, governments, associations, consumers) interacting with beekeepers. The results of the study are used to make recommendations to stakeholders. Due to the high mortality rate of 30% of bees, beekeepers renew at least 30% of their hives each year. They specifically request training on bee health and hive management (for amateur beekeepers). All beekeepers want organic farmers and want a medium-term cooperation between beekeepers, organic farmers and the government (through the application of environmental laws). All stakeholder aware of the disappearance of this pollinator-bees must act quickly to protect and conserve them on earth. This means that the stakeholders at their level must implement strategies of ecological transition in their behavior, because it is a question of saving the pollinators for the Welfare of the Society.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".