Development of sperm sexing and associated assisted reproductive technology for sex preselection of captive bottlenose dolphins (Tursiops truncatus)
Bibliographic record
Abstract
Research was conducted to develop sperm sorting and novel sperm preservation methodologies for sex predetermination in the bottlenose dolphin (Tursiops truncatus) using artificial insemination. In Study 1, the effect of seminal plasma (SP), sperm concentration and freezing rate (FR) on in vitro sperm quality of liquid-stored, non-sorted spermatozoa was examined. There was no effect (P > 0.05) of prefreeze SP addition on post-thaw quality (progressive motility, kinetic rating, sperm motility index (SMI), viability and acrosome integrity). Post-thaw motility parameters and viability were higher (P < 0.05) for slow FR than fast FR samples. In Study 2 investigating the effects of liquid storage and sorting on sperm quality, motility and SMI after sorting and centrifugation were lower (P < 0.05) than those of the initial ejaculate. The sort rate for enrichment (91 +/- 4% purity) of X- and Y-bearing spermatozoa was 3400 +/- 850 spermatozoa sex(-1) s(-1). In Study 3, compared with a modified straw method, directional freezing resulted in enhanced in vitro quality of sorted and non-sorted spermatozoa derived from liquid-stored semen (P < 0.05). In Study 4, endoscopic insemination of three dolphins with sorted, frozen-thawed X-bearing spermatozoa resulted in one conception and the birth of a female calf. High-purity sorting of dolphin spermatozoa, derived from liquid-stored semen, can be achieved with minimal loss of in vitro sperm quality and samples are functional in vivo.
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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 | 0.001 | 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".