Conference Paper: Green Industry Adapted to Recycling Needs of Lubumbashi City and Surrounding Areas in Democratic Republic of the Congo
Bibliographic record
Abstract
The interest of this conference is agricultural, environmental, bioenergetics, and sanitary. In that context, domestic, agricultural and industrial environments produce organic waste, which needs to be collected, selected, stored and recycled properly in order to avoid environmental pollution and promote agriculture. The green Industry proposed involves the conversion of natural, non-toxic organic waste in order to efficiently produce organic fertilizers for agriculture. These types of fertilizers from biological origin are suitable because they are not toxic for human and the environment. Enzymatic reactions described in this presentation concern mainly the hydrolysis of proteins, sugars and lipids, the acidification of intermediate products from hydrolysis, the formation of acetate, and the production of methane. In other words, this review is timely as it discusses for the chemical behavior or the reactivity of different functional groups to better understand the enzymatic catalysis in the transformations of residual proteins, carbohydrates, and lipids to generate biomethane and fertilizers. In the same perspective, this review is to enrich the documentation related to organic reactions catalyzed by enzymes, which occur in the anaerobic degradation of residual organic substances, with emphasis on the structures of organic compounds and reaction mechanisms. This will allow understanding the displacement of the electrons of a reactive entity rich in electrons to another reactive entity that is poor in electrons to form new bonds in products.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 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".