METHODS FOR CALCULATING WATER USE FOR DAIRY PRODUCTS PRODUCTION ENTERPRISES, TAKING INTO ACCOUNT THE PROCESSED RAW MATERIALS AND TYPES OF PRODUCED PRODUCTS
Bibliographic record
Abstract
Regulation of water use at industrial enterprises, especially at water-intensive enterprises, in a changing climate is an important economic and environmental task. One of the ways of sustainable water use at enterprises is the constant planning of water consumption and wastewater disposal in relation to the volumes and types of products produced. From a scientific point of view, at water-intensive enterprises, the most reasonable approach to optimizing water use is the development of individual technological standards for water use (water consumption and disposal). Dairy enterprises are quite water- intensive, the water use of which depends on a number of factors: assortment of raw materials for the production of products and types of products; diversification of production processes; the formation and processing of by-products, which often leads to an increase in the volume of wastewater formation in relation to the volume of water consumption; technologies for sanitizing equipment. Taking into account the specifics of production processes, a Methodology for calculating water use for enterprises for the production of dairy products, taking into account the processed raw materials and manufactured products, has been developed. The Methodology substantiates the need to change the terminology in terms of water use rationing, developed criteria for choosing two approaches to the development of individual technological standards for water use, detailed articles of water consumption and water disposal, and clarified certain parameters for their calculation. Approbation of the Methodology showed that the proposed approaches and individual parameters for calculating water use items allow the enterprise to more accurately predict the volumes of water consumption and wastewater disposal when planning production activities, which is especially important for water-intensive industries in conditions of limited available water resources.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".