COVID-19 Awareness and Preparedness of Minnesota and Wisconsin Dairy Farms
Bibliographic record
Abstract
Dairy farms that had participated in previous and ongoing projects with the National Farm Medicine Center (NFMC), Migrant Clinicians Network (MCN), and Upper Midwest Agricultural Safety and Health Center (UMASH) were asked to participate in a 17-question survey by phone or email to investigate biosecurity principles on Minnesota and Wisconsin dairy farms in response to COVID-19 and the effects of the pandemic on the dairy industry. Three additional farms were recruited via a press release published in agricultural newsletters. Of 76 farms contacted, 37 chose to participate in this study from June to July 2020. In response to the COVID-19 pandemic, dairies have implemented or increased biosecurity measures and COVID-19 precautions. Dairies reported adequate personal protective equipment for their workers, though face masks were not required on most dairies (n = 32, 86%). Producers were concerned about the safety of their families, maintaining a healthy workforce, and keeping their farms profitable. Access to healthcare was not perceived to be an issue for their workers. One-quarter of dairies reported COVID-19 infections on their farms. Even though the majority had an isolation protocol in place if someone on the farm were to become ill, less than half of respondents felt their farm was protected against COVID-19. Two-thirds of producers have not had to decrease production, and a majority of operations have not furloughed or terminated employees due to COVID-19. Our data suggest that dairy farms in Minnesota and Wisconsin have implemented biosecurity and safety measures in response to COVID-19. These measures can be improved. Farms would benefit from additional guidance and education on implementation of personal protective measures and disease prevention strategies to keep workers employed and safe.
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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 | 0.003 |
| 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) | 0.002 | 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".