Economic evaluation of cleaning and disinfection of facilities from broiler chickens challenged with <i>Campylobacter jejuni</i>
Bibliographic record
Abstract
For a new management practice to be adopted in broiler production, it must be efficient and economically viable. Few studies have been conducted with the purpose of evaluating costs and revenues relative to cleaning and disinfection. Based on the need to show economic efficiency of these practices, we compared two cleaning and disinfection protocols. The first method is more detailed and developed in seven steps, whereas the second method is more simplified with three steps. The costs, estimated total revenue from poultry carcass sale, and gross margin of sale were calculated for each cleaning and disinfection program. As expected, the cost of executing the first protocol was greater than the second one. However, due to the positive influences of preventive procedures, such as cleaning and disinfection on broiler performance, it was hypothesized that the two protocols would have similar gross margins from the sale of chicken carcasses. This study demonstrates an increase in economic viability when invested in a more detailed and complete cleaning and disinfection protocol. From the results obtained with the present study, it is possible to demonstrate a greater profitability and economic feasibility when a more detailed cleaning and disinfection protocol was carried out, due to similarity of its gross margin from the sale of product compared with simplified protocol.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".