Echotextural characteristics of the mammary gland during early lactation in two breeds of sheep varying in milk yields
Bibliographic record
Abstract
The main goal of this preliminary study was to determine and compare ultrasonographic characteristics of the mammary gland in two genotypes of ewes varying in milk productivity at 2, 3 and 4 weeks after lambing.Ultrasonographic images of the udder were obtained using the 5.0-and 7.5-MHz transducers, in axial and coronal planes, in four low milk-yielding Polish Mountain sheep and six high milk-yielding Olkuska ewes.All ultrasonograms were subjected to computerized image analyses using commercially available image analytical software (Image ProPlus ; Media Cybernetics Inc., San Diego, CA, USA) to determine numerical pixel values (NPVs) and heterogeneity (pixel standard deviation-PSD) of the mammary gland parenchyma.During the 28-day period post-partum, the Olkuska sheep exceeded (P < 0.05) Polish Mountain ewes in milk productivity (31.6 ± 2.7 l and 25.0 ± 4.2 l, respectively; means ± SEMs) as estimated by the mean weight gains of suckling lambs.In animals examined with the 5.0-MHz transducer, mean NPVs of the mammary gland parenchyma in Olkuska ewes and mean PSD in both genotypes of ewes were lower (P < 0.05) before than after milking.In addition, PSD recorded both before and after milking were lower (P < 0.05) in the Polish Mountain compared with Olkuska breed.Mean PSD values for the mammary gland were less (P < 0.05) before than after milking in Polish Mountain ewes and they were greater (P < 0.05) in Olkuska compared with Polish Mountain ewes examined with the 7.5-MHz probe after milking.It can be concluded that milk quantity, histomorphology of the udder and ultrasound transducer frequency may all impinge on the echotextural characteristics of the mammary parenchyma in different breeds of sheep.Our observations warrant future studies of correlations between milk composition, mammary gland histophysiology and ultrasonographic image attributes of the mammary gland in ruminants.
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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 | 0.001 |
| 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 | 0.000 |
| 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 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".