Metabolic Studies on Lean and Fat Pekin Ducks Selected for Breast Muscle Thickness Measured by Ultrasound Scanning
Bibliographic record
Abstract
Female Pekin ducks selected for high (lean) or low (fat) breast muscle:total breast thickness ratio (MT:TOT), measured with ultrasound scanning, were used in metabolic studies to determine the metabolic differences and assess the effects of feed withdrawal and refeeding on plasma parameters. Lean and fat ducks had similar body weight changes throughout the studies. Lean ducks had significantly lower fat and higher CP and ash carcass contents. They also had higher (P < 0.05) plasma glucose after feed withdrawal but less (P < 0.05) plasma glucose after feeding. Fat ducks had similar plasma triglycerides (TG) after feed withdrawal but higher (P < 0.05) TG concentration after feeding and higher (P < 0.05) plasma uric acid than the lean females after feeding. Feed deprivation resulted in higher (P < 0.05) plasma cholesterol than did feeding. Lean ducks fed the test diet retained more (P < 0.05) N than the fat birds. Lean and fat Pekin ducks had similar (P > 0.05) AME, AMEn, TME, and TMEn. Lean ducks lost less (P < 0.05) N than fat birds. Plasma cholesterol was not significantly correlated (P = 0.06) with nitrogen retention (NR); NR was negatively correlated (P < 0.05) with plasma TG, and TG was positively correlated (P < 0.05) with total energy excreted. There was a positive correlation (P < 0.05) between MT:TOT and NR. The MT:TOT correlated (P < 0.05) negatively with carcass fat, and positively with carcass CP and plasma insulin-like growth factor I (IGF-I) concentration. The results we obtained demonstrate the usefulness of ultrasound scanning in live bird selection for leanness and greater breast muscle thickness and are related to metabolic differences between lean and fat female Pekin ducks selected for high or low MT:TOT ratios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".