Effect of extender composition and freezing rate on post-thaw motility and fertility of Arctic char, Salvelinus alpinus (L.), spermatozoa
Bibliographic record
Abstract
The effects of extender composition and freezing rate on motility and fertility of frozen-thawed Arctic char, Salvelinus alpinus, spermatozoa were investigated. Three freezing rates, two semen diluents and three cryoprotectants were tested. Semen frozen in 0.3 mol L−1 glucose diluent with 10% methanol as a cryoprotectant or in a diluent described by Lahnsteiner with 10%N,N-dimethylacetamide (DMA) resulted in the highest sperm motility. Fertility was the highest for semen frozen in a glucose–methanol extender but was not significantly different than that for semen frozen in Lahnsteiner's diluent with 10% DMA. Dimethyl sulphoxide (DMSO) at 10% was a relatively ineffective cryoprotectant with either semen diluent. Semen frozen at 6 cm above the surface of liquid nitrogen resulted in a higher post-thaw sperm motility and fertility than semen frozen at 5 cm. The addition of 7% fresh egg yolk to glucose diluent containing methanol or DMSO did not improve the fertility of frozen-thawed spermatozoa. However, the addition of 7% fresh egg yolk to glucose–DMA extender significantly improved the fertilization percentages of frozen-thawed spermatozoa. In conclusion, dilution of semen 1:3 in 0.3 mol L−1 glucose with 10% methanol and freezing 6 cm above the surface of liquid nitrogen (freezing rate of 40±8°C min−1, mean±SD from −5 to −55°C) is a promising protocol for cryopreservation of Arctic char semen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".