Testicular parameters and spermatogenesis in different birthweight boars
Bibliographic record
Abstract
The present study investigated the effect of birthweight on testicular development and spermatogenesis in boars. Twenty-four pairs of littermate boars were selected: one piglet with the highest birthweight (HW) and the other with the lowest birthweight (LW) within the litter. Two subsets of 12 pairs of male littermates from each birthweight group were obtained after selection: one subset was orchiectomised at 8 days and the other at 8 months of age. HW boars had higher body and testicular weights at both ages (P<0.05). Testosterone concentrations and the relative expression of 17α-hydroxylase in the testis were similar between birthweight groups. Birthweight affected somatic and germ cell numbers in the neonatal testis, which were higher in HW boars (P<0.05). Moreover, a significant reduction in the number of pachytene spermatocytes and round spermatids was observed in LW boars (P<0.05) at 8 months of age, which caused a decrease in the total number of elongated spermatids and daily sperm production (P<0.05). Hence, HW boars have the potential to produce more spermatozoa and consequently more semen doses per ejaculate, and would be very valuable to an industry that relies on AI.
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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 | 0.001 | 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".