Výskyt mastitid a jednotlivých patogenů u dojnic holštýnského skotu a jejich vztah k mléčné užitkovosti
Bibliographic record
Abstract
The bachelor thesis deals with the incidence of diseases of the mammary gland. The first half of the thesis deals with a literature search on the origin and functioning of the mammary gland. Among other things, it describes how the mammary gland itself is formed, from the development of the individual in the womb to the production of milk and its components. The work includes chapters on somatic cells, which are one of the main indicators of disease. The main chapter is mastitis, which describes its etiology, pathogenesis, symptomatology, therapy and prevention. The literature review also concludes with a summary of what influences mastitis and what its consequences are. Data were collected between December 2023 and March 2024 and then evaluated in several summary tables, which show, for example, that mastitis causes up to a 50% decrease in an individual's milk yield. It was also found that the somatic cells of diseased dairy cows increased many times compared to the herd average. There was no evidence of an effect of the affected quarter on milk yield. There was no significant difference in the frequency of affected quarters. Of the pathogens, Streptococcus uberis was almost exclusively present in the herd evaluated (84 %).
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".