Physico-Chemical Characterization of Nine Agricultural Biomasses of the Togolese Flora
Bibliographic record
Abstract
Green coal or bio-coal is coal produced with rich biodegradable materials, elaborated from agricultural and household residues with a high percentage of carbon. This green charcoal (fuel briquettes) is an alternative to charcoal. Well known for its contribution to greenhouse gas emissions, charcoal is one of the causes of tree felling. The valorization of waste by the manufacture of biofuels could be an alternative to the use of charcoal. The general objective of the present study is the valorization of nine biomasses from Togo as raw materials. Specifically, physico-chemical characteristics such as dehydration, acidity, and conductivity were determined. Information on the structure and composition of the biomass was found. These data on the nature of the biomass were found through the use of Fourier Transform Infrared (FTIR) and Thermogravimetry (TGA). The promising results inform on the nature of the analyzed samples and allow the selection of the best biomass which would give an important thermal conductivity for the manufacture of the briquettes, but also of the binders to be used according to the physico-chemical characteristics like the pH.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".