Bibliographic record
Abstract
The paper describes characteristics of a modern agricultural enterprise, the main purpose of its operation and the risks it represents to the environment. The problem of a bigger gap between crop and livestock farming is highlighted, which results in ever increasing topicality of environmental safety of enterprises in agro-industrial sector. Many developed countries, such as Germany, USA, Canada and the Netherlands consider the lowering of environmental load on the environment as one of the primary goals of their long-term development. The article discusses the urgent need to introduce new, more intensive, but environmentally safe and economically sound technologies for utilization of animal/poultry manure, including the technology of bioconversion of waste in a drum-type biofermentor. The aim of the study is to adjust the operation modes of a biofermentor. The study will include a full factorial experiment. The air flow rate and the frequency of drum revolutions were selected as controllable factors. The experiment will be implemented by the 32 matrix. The intervals and varying levels of controllable factors, experiment planning matrix, two-factor model of the experiment, as well as the design of biofermentor laboratory model are described. By the experiment results a mathematical model of bioconversion process in the drum-type biofermentor will be designed to simulate the process of accelerated composting of different types of organic waste and to predict the optimal parameters and operating modes depending on the type and characteristics of the raw material, as well as the requirements to the end product.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".