Differences in muscle characteristics of piglets related to the sow parity
Bibliographic record
Abstract
da Silva, A., Dalto, D., Lozano, A., de Oliveira, E., Gavioli, D., de Oliveira, J., Jamile, Romero, N. and da Silva, C. 2013. Differences in muscle characteristics of piglets related to the sow parity. Can. J. Anim. Sci. 93: 471–475. Two hundred forty-three piglets were obtained from 81, 1st through 7th parity sows to determine the influence of sow's parity on piglets’ myogenesis. Those piglets weighing close to or equal to the average weight of their litter were sacrificed, and their semitendinosus muscles were collected to determine the secondary muscle fiber number, area and weight. The number of secondary muscle fibers was correlated with muscle weight (P<0.05; 0.39) and muscle area (P<0.001; 0.63), and muscle area and weight were also correlated (P<0.001; 0.64). Weights of piglets at birth had a correlation with number of muscle fibers (P<0.05; 0.39), muscle area (P<0.001; 0.54) and muscle weight (P<0.001; 0.73). The piglets’ birthweights and muscle weight, muscle area and muscle secondary fiber numbers increased quadratically as parity increased (R 2=0.56, 0.36, 0.44, 0.64 and 0.54; P<0.05, respectively). The results of this study indicate that parity influences the pre-natal development of piglets and that the best muscle characteristics of piglets born from 3rd and 4th parity sows were responsible for their higher weight at birth.
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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 | 0.001 |
| 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) | 0.002 | 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".