COVID-19 Acutely Impacted the Delmarva Poultry Industry in Early 2020
Bibliographic record
Abstract
Early community spread of COVID-19 presented a public health crisis and Delmarva's essential workforce at the poultry processing plants. Plant workers in May 2020 were struggling to adapt to exposure risk and illness in the workforce. Furthermore, pressures of an unfamiliar marketplace strained the supply and demand linkages in poultry processing. By utilizing strategies to meaningfully slow the supply of chicken at the processing plant, farm and hatchery, supply was slowed without stopping. This ensured security in the food supply, but jeopardized farmers raising these livestock. After weeks of processing adjustments, some chicken farms were depopulated as a last resort to protect their welfare. The remains of the depopulated flocks presented a risk to public health from environmental externalities. Across the Delmarva peninsula, carcasses were composted in the housing in which they were raised along with feed, bedding and manure, and high-carbon material, and were carefully monitored to reduce environmental impacts. Compost is recycled into a resource and can be utilized safely on farms for soil conditioning, like organic fertilizer, rather than presenting an environmental disaster.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".