Instrumental methods as strategic supporting tools for systematic biochemical analysis of serra da estrela sheep milk.
Bibliographic record
Abstract
Serra da Estrela sheep crude milk is mandatory for the PDO Serra da Estrela (SE) cheese production. The production system implies the need for systematic knowledge of the values of useful matter (fat and protein) to produce cheese. The knowledge of somatic cell count (SCC) allows to predict the mammary health status of ewes as well as make decisions on the use of crude milk for human consumption, through its transformation into cheese. The need for simple, fast and reliable methodologies for the determination of this parameters is fundamental for the sustainability of this productive sector through milk recording procedure for animal genetic evaluation, as well for the previous analysis of the bulk milk, before the process of elaboration of the PDO SE cheese. Two sets of milk samples were collected: 50 individual samples of Serra da Estrela ewes’ milk from 2 farms for analysis of SCC and 53 samples of Serra da Estrela ewes’ milk (16 individual samples and 37 samples of bulk milk) for analysis of fat and protein contents (Fat% and Prot%). Duplicates of samples were simultaneously analyzed by reference and instrumental methodologies (DCC De Laval optical reader and FT-NIR MasterTM from Büchi) as reliable alternatives for parameters evaluation for the SE sheep milk. The results showed a significant agreement between the pairs of values (type of methodologies) for all parameters, with correlations between 0.925 (Prot%) and 0.960 (SCC) (p<0.001). The linear regressions for the pairs of data of the three parameters studied presented a strong adjustment (with the coefficients of determination between 0.856 and 0.921). The findings showed that both instrumental methodologies applied can be used as alternative to count somatic cells and evaluate fat and protein contents of SE sheep milk and will be useful as strategic measuring devices for milk farmers and cheesemakers.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".