Environmentally Friendly Methods and Technologies of Bioconversion of Organic Wastes from Agro-Industrial Complex for Production of New Types of Organic Fertilizers
Bibliographic record
Abstract
The effectiveness of the use of traditional and intensive technologies for the aerobic processing of compost mixtures based on sewage sludge (SS), peat and activators of biothermal processes was studied. It was found that the recycling of waste in clamps using traditional technology did not provide high-temperature sanitation of compost mixtures. Intensive aerobic processing in fermentation chambers activated biothermal processes, ensured reliable disinfection of the compost mixture, which included bird droppings, soil, peat. The production of biocompost met the requirements of GOST R 55570, GOST R 54651, EU Regulation No. 2019/1009, the National standard of Canada CAN/BNQ 0413-200. In comparison with processing in clamps, intensive aeration carried out in an extremely short time was accompanied by lower losses in compost mixtures of organic matter, nitrogen (by an average of 10%), an increase in the content of mobile potassium, selective accumulation of mobile forms of heavy metals (copper, zinc, lead), which did not affect phytotoxicity of biocompost.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".