MATURAÇÃO DO QUEIJO MINAS ARTESANAL DA MICRORREGIÃO CAMPO DAS VERTENTES E OS EFEITOS DOS PERÍODOS SECO E CHUVOSO
Bibliographic record
Abstract
There is an expressive artisanal cheeses production in Brazil, manufacturedfrom raw milk, and within this context, the state of Minas Gerais has a highlight inthis activity with its five micro regions officially recognized. Within these, “Campodas Vertentes” was the last to have recognition. Evaluating the effect of dry andwet periods during the ripening of artisanal Minas cheese of the micro regionabove mentioned was the main objective of this study, which also related aspects ofphysicochemical composition of 10 to 30 days of ripening. Four registered dairieswere selected and who attended legal requirements and good manufacturing practiceto compose the experiment. The analysis of moisture content, moisture to the non fatsubstance (MNFS) and pH showed that these values varied greatly among cheesesand were highest in the dry season. Indexes of proteolysis behaved with variationbetween samples and advanced in the period of ripening, however, they were higherin the wet season. Even aware that the moisture content of the cheeses exerts stronginfluence on proteolysis, as well as other factors such as dosage of coagulant and“drop”, the room temperature observed in two periods of ripening was also veryimportant for the advancement of proteolysis.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".