Bibliographic record
Abstract
The poultry meat industry has gone through many changes. It moved from growing dual-purpose birds (meat and egg production) taking ~110 days to reach 1.2 kg 100 years ago, to developing specialized meat breeds that grow to 2.5 kg within ~40 days. It also moved from selling ~80% whole birds to mostly selling cut up and further processed products in the Western world. This necessitated building large, centralized processing plants, capable of processing 15,000 birds per hr on a single line (60 years ago only 2500), that require higher bird uniformity (size, color, texture). Furthermore, consumer demand for convenient products resulted in introducing many cut-up fresh poultry (some companies have 500 SKU) and further processed products (chicken nuggets did not exist 50 years ago). Those developments were possible due to advancements in genetics, nutrition, medicine, and engineering at the farm and processing plant levels. Challenges keep on coming and today a rise in myopathies (e.g., so called woody breast, white striping, spaghetti meat), requires solutions from breeders, farmers, and processing plants, as more automation also requires more uniformity. This review focuses on the changes and challenges to the processing industry segment required to keep supplying high quality poultry to the individual consumer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".