Fish oil divergently enriches broiler meat with long chain ω-3 polyunsaturated fatty acids (LCω-3PUFAs) by modulating the ratio of ω-3 to ω-6 PUFAs without disrupting gut morphology and cardio-pulmonary morphometry
Bibliographic record
Abstract
A trial was conducted for 35 days to investigate if replacement of soybean oil (SO) for fish oil (FO) influenced average daily feed intake (ADFI), average daily gain (ADG), final live weight (FLW), feed efficiency (FE), haemato-biochemical indices, carcass traits, cardio-pulmonary morphometry, gut morphology, nutrient digestibility, and fatty acid profile of the broiler chicken. A total of 350, day-old Ross-308 male broilers were distributed in a completely randomized design into five dietary treatment groups designated as FO0% (diet without FO, i.e., 100% SO), FO25% (diet containing 25% FO + 75% SO), FO50% (diet containing 50% FO + 50% SO), FO75% (diet containing 75% FO + 25% SO), and FO100% (diet containing 100% FO). Each treatment was replicated seven times containing 10 birds per replicate. Results indicated that complete replacement of SO for FO increased 4.7% FLW, 12.4% ADFI, 3.9% ADG, 16.2% HDL, and 8.6% CP. Although, FO contained 471.1% more ∑LCω-3PUFAs than SO, the FO-supplemented breast meat was enriched with net increment of 45.0% ∑LCω-3PUFAs and 81.0% ∑ω-3: ∑ω-6 at the expense of 7.1% FE and 26.8% MDA. Complete replacement of SO for FO did not compromise net profit. Hence, replacement of SO for FO may be commercially plausible.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".