Study of carcass, organ, muscle, fat tissue weight, and concentration in rats fed CLA or its precursors by principal component analysis
Bibliographic record
Abstract
Carcass, organ and muscle weight, and fat tissue data were obtained from 30 weaned male Wistar rats fed one of three diets, (10 rats/diet) over a period of 60 d. The diets were base with synthetic conjugated linoleic acid (CLA), sunflower oil or beef enriched CLA. The CLA diet contained the base diet and 18.2 g kg-1 of synthetic CLA (53% cis 9, trans 11 and 44% trans 10, cis 12) replacing 26% of the soybean oil, the sunflower oil diet contained the base plus 70 g kg-1 of sunflower oil replacing all the soybean oil, and the CLA-enriched diet contained the base plus 200 g kg-1 of beef enriched bio-formed CLA replacing the casein. Data were subjected to a principal component analysis (PCA). The first principal component (PC) extracted carcass weight, organ and muscle weight variables and accounted for 41.3% of the total variation. The second principal component included all of the fat tissue variables and accounted for 20.5% of the total variation. The rats fed the synthetic CLA diet were associated with high carcass, liver, kidney, heart, gastrocnemius and soleus muscle weights, and low retroperitoneal and inguinal fat weights, and adipocyte numbers in the fat tissues. In rat models, short periods of synthetic CLA feeding may have a greater impact on decreasing fat accretion in selected fat tissues than feeding CLA-enriched meat. The PC analysis provides means of combining into one or a few components traits that have similar responses, each component being orthogonal to all other components, whereas, in a conventional univariate analysis of variance each dependent variable is analyzed separately in relation to one or more independent variable. Key words: Conjugated linoleic acid, feeding, principal component analysis, fat, muscle, accretion
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".