Semen collection, characterisation and artificial insemination in the beluga (Delphinapterus leucas) using liquid-stored spermatozoa
Bibliographic record
Abstract
Ejaculates were collected from a beluga (Delphinapterus leucas) to gain an understanding of sperm biology and develop a short-term sperm preservation method for use in artificial insemination (AI). Ejaculate parameters and biochemistry, semen production and serum testosterone concentrations of an adult male were characterised for 21 months. Sperm viability, acrosome integrity and morphology did not change (P > 0.05) but ejaculate volume, sperm concentration and total spermatozoa per ejaculate were higher (P < 0.05) from January to June than from July to December. Peak testosterone concentrations (P < 0.05) were observed from October to April (8.0 +/- 1.6 ng mL(-1)). The effects of hyaluronic acid (HA), antioxidants, storage temperature and time on in vitro sperm characteristics were examined. Motility parameters and viability were improved (P < 0.05) when semen was stored at 5 degrees C compared with 21 degrees C. During the first 24 h of storage sperm agglutination was absent only at 5 degrees C in the presence of HA. A nulliparous 28-year-old female was inseminated endoscopically with liquid-stored semen. A pregnancy and birth of a calf was achieved following AI for the first time in this species, thereby validating both the AI technique and the fertility of beluga spermatozoa after chilled storage in a specialised diluent.
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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 | 0.001 | 0.001 |
| 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 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".