Caracterização dos resíduos sólidos gerados em laticínios
Bibliographic record
Abstract
Environmental awareness is increasingly inserted in the consumer’s choices. The concern to meet this demand has driven companies to seek the adequacy of their processes and routines. This study aims to carry out a survey of the solid residues generated in the dairy industry and the respective treatments and the final disposal. Twenty-two (22) industries from different regions of Brazil were invited to participate, classified according to size and potential pollutant. Seven (7) dairy industries were located in the Southeast, 3 (three) in the South, 3 (three) in the Midwest, 3 (three) in the North, 7 (seven) in the Northeast. A structured electronic questionnaire was elaborated according to the class division of the residues: common, chemical and biological, and sent to the dairy. The answers showed that the dairy industries, regardless of the volume of processed milk with intrinsic characteristics, frequency and volume generated, do not have residue management in their routines. This study demonstrates the current state of how dairy industries address this issue and highlight the need for these industries to adapt to environmental issues. Based on current solid waste legislation, the dairy industry should seek to adapt and integrate sustainable technologies into its processes and routines. The adoption of these practices has a direct and positive influence on the marketing and commercialization of the company.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".