Inseminating fresh or cryopreserved semen for maximum efficiency: Implications for gene banks and industry
Bibliographic record
Abstract
Developing gene bank germplasm collections for animal genetic resources requires establishing germplasm collection goals, that consider capturing the genetic diversity of the population in question and the amount of germplasm required for its reconstitution or other purposes, or both. Computing collection goals for chickens is complicated, compared with mammalian species, due to the multiple chances a single insemination of semen has to fertilize an egg. To address this issue, fertility data were used in conjunction with econometric procedures for determining production efficiency and diminishing returns. Experimental treatments consisted of inseminating fresh semen intravaginally (FIV), frozen-thawed semen inseminated intramagnally (FTIM), and frozen-thawed semen inseminated intravaginally (FTIV). Analysis revealed that the maximum efficiency for a single insemination was at postinsemination d 6, 8, and 3 for FIV, FTIM, and FTIV, respectively. But, additional benefit from a single insemination can be garnered by continuing to collect and incubate eggs to d 11, 17, and 11 for FIV, FTIM, and FTIV, respectively. By extending the insemination interval, the number of fertile eggs can be increased by 62 (FIV), 62 (FTIM), and 48% (FTIV). The ramifications of these results are profound when placed in the context of germplasm collection for gene banks. By using the FTIM treatment, the number of germplasm samples needed to secure a chicken breed, at the 150% level, can be reduced from the FAO projection of 2,454 to 386 straws (0.5 mL). Such a change represents a substantial reduction in collection, processing, and storage costs for gene banks. For industry, the results suggest that extending the time interval between inseminations will yield more fertile eggs and create opportunities to increase the number of hens mated to a rooster.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".