Suitability of epididymal and testicular ultrasonography and computerized image analysis for assessment of current and future semen quality in the ram
Bibliographic record
Abstract
Breeding soundness evaluation (BSE) is the primary assessment for determining the reproductive potential of male animals. This method, however, cannot be used to evaluate semen frequently or to predict future semen quality. Computerized analysis of ultrasonographic images provides information on histophysiological changes in male reproductive organs. We hypothesized that: (i) semen parameters would correlate with ultrasonographic characteristics of the distal region (cauda) of the epididymis and (ii) testicular ultrasound images and/or circulating testosterone concentration would predict future semen quality in the ram. Six adult rams underwent BSE and scrotal ultrasonography approximately 60 d apart (average duration of the spermatogenic cycle) both during the breeding (December and February) and non-breeding (June and August) seasons. An inverse correlation was found between pixel intensity (numerical pixel values) of the epididymes and percentage of sperm in semen with normal morphology (r = -0.46, P < 0.05). Pixel heterogeneity (standard deviation of pixel values) correlated negatively with percentage of sperm with normal morphology (r = -0.42, P < 0.05) and directly with percentage of spermatozoa with abnormal tails (r = 0.43, P < 0.05). Pixel heterogeneity of testicular parenchyma obtained approximately 60 d prior to semen evaluation inversely correlated with percentage of sperm with normal morphology (r = -0.73, P < 0.01) and sperm progressive motility (r = -0.76, P < 0.01), and directly with percentage of sperm with abnormal tails (r = 0.72, P < 0.01) and loose heads (r = 0.79, P < 0.01). We concluded that scrotal ultrasonography combined with computer-assisted analyses of epididymal and testicular echotexture in the ram was a valuable method for determining certain current and future semen parameters, respectively.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".