FUNCTIONING OF SELECTED BEEKEEPING FARMS IN POLAND DURING COVID-19 PANDEMIC
Bibliographic record
Abstract
Aim: The purpose of this study was to examine the impact of the COVID-19 pandemic on selected beekeeping farms, as well as to compare the experience of chosen Polish beekeepers with the impact of the pandemic on beekeeping in other countries, as shown in the literature. Methods: The study was conducted using a literature review and questionnaire interview (n = 36) among beekeepers in the Mazowieckie and Warmińsko-Mazurskie provinces according to a 5-level Likert scale. Responses on industry topics were correlated with opinions on the positive and negative impact of the pandemic on beekeeping using the Pearson correlation. Results: Approximately 60% of respondents said that the pandemic had little or even no impact on their beekeeping activities. This may have been related to the peculiarities of Polish beekeeping, which is not dependent on seasonal labor. There were also moderate correlations between negative opinions on the impact of the pandemic and a lack of contact with other beekeepers and the seasonality of production and related sales as well as between opinions on the time-consuming nature of production and the positive impact of the pandemic on the beekeeping market. Conclusions: It was stated that beekeepers proved to be more resilient to supply chain breakdowns compared to beekeeper experiences in countries such as Canada and the UK. The nature of beekeepers’ marketing channels may affect the speed with which they can sell their goods. The impact of the pandemic on Polish beekeepers has not been previously studied.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".