Bibliographic record
Abstract
The poultry industry has seen significant changes in the methods used to harvest fresh poultry meat over the past four decades. Some of the major changes include a more than four-fold increase in line speed (new plants are designed to process 12,000 broilers per hour), a large increase in the proportion of cut up and deboned meat produced, as well as substantial improvements in sanitation. These advancements have been possible by gaining knowledge in areas such as computer science (e.g. image analysis, on line weighing and tracking), live bird handling (transportation, unloading, stunning), muscle biology (post mortem processes), heat and mass transfer (scalding, chilling), and engineering (machine building, metallurgy). This article includes a general overview of the different steps involved in primary poultry processing and focuses on some of the principles that have been used to achieve greater efficiencies in mechanising the whole process. The focus areas include stunning, electrical stimulation, chilling, and mechanical filleting. These topics will be used to demonstrate the importance of obtaining high meat quality (e.g. fewer downgrades, high water holding, acceptable tenderness and colour) currently demanded by processors as well as consumers. The advantages of in-line-processing will also be highlighted, where improved efficiencies have been achieved by incorporating real-time computerised monitoring and tracking systems.Overall, a comprehensive understanding of the whole process and the integration of the different steps is a challenge that must be met by both the equipment manufacturer and processing plant personnel. Because of the increased complexity of the whole integrated process, it is highly recommended that the processor team up with a very knowledgeable equipment manufacturer who has the technical understanding and experience within all stages of the process (farm gate to fork), to effectively optimise quality, yield, and speed.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".