Meat Quality of Commercial Chickens Reared in Different Production Systems: Industrial, Range and Organic
Bibliographic record
Abstract
Abstract Meat is an important part of the human diet since it provides several nutrients. However, the amount of these nutrients can differ according to several factors. With this in mind, the present research was designed with the main objective of evaluating the effect of production system of broiler chickens (industrial, range and organic) on meat quality. The physicochemical, chemical and nutritional characteristics were determined in breast and drumstick meat. The organic chickens presented the lowest amounts of fat and cholesterol and the highest amounts of protein. The colour was also influenced by the production system, where organic and range chickens had the highest values of redness in both cuts (breast and drumstick). In addition, the content of essential fatty acids (C18:2 n-6 and C18:3 n-3 ) and other fatty acids with high biological importance, such as eicosapentanoic acid (EPA; C20:5 n-3 ), docosapentanoic acid (DPA; C22:5 n-3 ) and docosahexanoic acid (DHA; C22:6 n-3 ) were higher in organic samples compared to industrial or range chickens. The amino acids content did not vary with the production system. With regard to mineral contents, organic chickens had the highest values of iron in drumstick and significantly lower values of magnesium in both cuts than industrial chickens. On the whole, the meat of the organic chickens showed better nutritional characteristics than those produced in range or industrial conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".