Fatty Acid Profile and CLA Content of Goat Milk: Influence of Feeding System
Bibliographic record
Abstract
There is great interest in producing bioethanol from biomass and there is much emphasis on exploiting The effect of pasture and of diet linseed supplementation on fatty acid profile of milk was evaluated using 45 pluriparous goats, equally divided after kidding into three homogeneous groups (H, P and L). Group H (control) and L were housed in a stall receiving alfalfa hay, while group P was led to pasture. The groups received concentrate which for group L had linseed as ingredient. Average milk yield did not statistically differ between the groups. Groups P showed significantly higher fat content than group H and L (4.62% vs 3.70%, and 3.90% respectively for groups P, H and L; P < 0.01). On the contrary, milk from goats of group H and L had significantly higher content of lactose (4.65% and 4.61% vs 4.57%, respectively for groups H, L and P; P < 0.05). The levels of C18:1 cis9, C18:1 trans11 and MUFA in milk were significantly (P < 0.05) increased by both the pasture and linseed, while linoleic acid (C18:2) and PUFA were significantly (P < 0.05) higher in group P compared either to H or to L groups. The highest values of CLAs, either as t11 CLA and t10 c12 CLA or as SCLA, have been registered in milk of group P, the lowest in group H. These parameters in milk of group L were significantly (P < 0.05) lower or higher than those of group P and H, respectively.
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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 | 0.000 |
| Scholarly communication | 0.000 | 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".