The effect of supplementing freezing extender with Mn2+-, Zn2+- or Cu2+-nanosuccinate on select post-thaw characteristics of ram semen
Bibliographic record
Abstract
The effects of Mn 2+ -, Zn 2+ - or Cu 2+ -nanosuccinate added to freezing extender on select post-thaw semen characteristics were determined in six Texel rams (aged 2–4 years) during seasonal anestrus (April-May). Ejaculates (n = 6 per ram) collected into an artificial vagina were divided into ten isovolumetric fractions each. Semen was diluted in lactose-yolk-tris-citrate-glycerin medium and nanosuccinates (Mn 2+ - and Zn 2+ -nanosuccinate: 0.0 (control), 2.5, 5.0 and 7.5 μg/l; Cu 2+ -nanosuccinate: 0.0 (control), 1.25, 2.5 and 3.75 μg/l) were added to semen extender . Extended semen was loaded into 0.25-ml straws and frozen in liquid nitrogen. After thawing, sperm motility parameters were determined with computer assisted semen analysis (CASA), and the activity of superoxide dismutase (SOD), glutathione peroxidase (GPx) and catalase (CAT) was measured with a spectrophotometric technique. The addition of 5.0 μg/l of Mn 2+ - and Zn 2+ -nanosuccinate significantly increased the sperm progressive motility and both 2.5 and 5.0 μg/l improved sperm motion kinetics. Further, both nanosuccinates at a dose of 5.0 μg/l significantly decreased SOD activity and stimulated an increase in GPx and CAT activity in semen samples. Alternatively, the addition of Cu 2+ -nanosuccinate (highest dose) significantly reduced the progressive motility and velocity of ram spermatozoa, increased the percentage of sperm with acrosomal/head defects and seminal SOD activity, and depressed CAT (highest dose) and GPx (all doses) activity. In summary, the addition of Mn 2+ - and Zn 2+ -nanosuccinate to semen extender had beneficial effects on sperm motility/motion kinetics and structural integrity, whereas Cu 2+ -nanosuccinate generally had debilitating effects on the post-thaw semen characteristics in rams.
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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".