Comparison of Different Methods for Assessment of Sperm Concentration and Membrane Integrity With Bull Semen
Bibliographic record
Abstract
Assessing semen quality is crucially important for the exploitation of genetically superior sires in an artificial insemination (AI) program. In this study, we compare modern and conventional techniques to estimate bovine sperm concentration and membrane integrity. First, the NucleoCounter SP-100 was validated for sperm concentration and provided statistically reliable and repeatable estimates among aliquots and replicates of 25 fresh ejaculates. Sperm concentrations in 78 ejaculates were then determined with hemacytometer, flow cytometer, and NucleoCounter SP-100 and were significantly correlated (P < .001), with regression coefficients among these 3 techniques close to 1 (P < .01). However, the sperm concentration determined by hemacytometer was lower (P < .01) than by flow cytometer and NucleoCounter SP-100. Forty frozen-thawed semen samples were then assessed for sperm concentration and membrane integrity with hemacytometer, flow cytometer and NucleoCounter SP-100. Significant relationships were found for sperm concentration determined by hemacytometer and NucleoCounter SP-100 and for sperm membrane integrity determined by flow cytometer and NucleoCounter SP-100 (P < .01). Finally, the standard curves of sperm concentrations in 6 spectrophotometers, comparing optical density against counts drawn by hemacytometer and NucleoCounter SP-100 (n = 94 fresh ejaculates) showed different (P < .01) intercepts and regression coefficients (linear, quadratic, cubic). It was calculated that a breeding station can improve its production potential by 13% with the use of NucleoCounter SP-100 instead of hemacytometer for calibration of spectrophotometers. Flow cytometer and NucleoCounter SP-100 can be used with equal confidence to estimate sperm concentration and membrane integrity in domestic animals and human semen.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".