Influence of Environmental Temperature on Milk Production in the Italian Mediterranean Buffalo
Bibliographic record
Abstract
The purpose of this study is to verify the influence of ambient temperature on the percentage of subjects with decreased production, compared to what was recorded in the previous 7-10 days. The data processed in the 2017-2020 period was studied in a company in the lower Caserta area that raises 3000 animals per year, of which 1300 are dairy. All this was possible thanks to the daily collection of production data recorded in the milking parlor, using RFID technology, and then transferred to management software, essential for the farmer for problem-solving decisions. The buffalo is of tropical origin, and there are many factors that influence the production of the Italian Mediterranean buffalo, first of all, seasonality, temperature, health, or the combination of all these elements. For these reasons, it seemed appropriate to focus our attention on the influence of ambient temperature on production. In the first three years of observation, it emerged that as the ambient temperature decreases, the percentage of subjects that recorded a drop in production compared to the previous figure increases. However, this was not found in 2020, when the covered structures were equipped with permanent bedding consisting of straw. This shows that it is important to guarantee the state of well-being so as not to penalize the production of milk for the PDO buffalo mozzarella, which represents an interesting source of income.
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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 | 0.000 | 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".