Effective Environmental Factors on Milk Composition, Rennet Coagulation Time and Urea Content of in Anatolian Buffaloes Milk of Ilıkpınar Village Hatay Province
Bibliographic record
Abstract
The objectives of this study were to investigate determining environmental factors on composition, renneting time, urea concentration, acidity, density and pH of Anatolian Buffaloes milk. As a total of 115 milk samples from 53 cows that were calved in the period of 2004 and 2005 years in 8 units of Ilıkpınar Village were collected in morning milkings in June, September, December and March. The cows were at their lactation days 30±15, 60±15, 90±15, 120±15, 150±15, 180±15, 210±15, 240±15 and 270±15. The milk samples were analysed for total dry matter, fat, protein, ash, density, pH, acidity, renneting time and urea content. Rennet coagulation time, urea, protein and fat contents were determined using Berridge, photometric, formal titration and Gerber methods, respectively. Data were classified as follows; lactation stages: 1 (30±15, 60±15, 90±15 days): 2 (120±15, 150±15, 180±15): 3 (210±15, 240±15, 270±15); calving year: 1 (2004), 2 (2005); calving season: 1 (January-May), 2 (September and October); month of samples collection: 1 (June), 2 (September), 3 (December), 4 (March); lactation order: 1 and 2 : 1, 3 and 4: 2, 5 and 6: 3. The effects of environmental factors of each variable were investigated separately and analysed using analysis of variance. Production mount in all the characteristics; calving year and lactation stage in most of the characteristics; lactation order on fat and protein contents; unit and calving season in some of the characteristics were found to be effective significantly. SPSS program was used in the statistical procedures.
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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 | 0.000 |
| 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".