Dried Products and Sustainable Development in Saharan Regions: The Case of Ghardaïa in the M’zab Region of Algeria
Bibliographic record
Abstract
In the M’zab in Algeria, the Saharan region of Ghardaïa has an immense solar energy potential that if properly utilized could significantly contribute to sustainable development and provide concrete solutions to socio-economic problems that arise with acuity in these desert regions. This region is endowed with a dynamic of development, able to favor the emergence of systems diversity, and agricultural and agrofood products of high quality. In recent years, the drying of fruits and vegetables on a small scale has witnessed renewed interest in the M’zab. The drying of agricultural products represents a very important socio-economic activity and serves as a main vector for the dynamics of local development. This study conducted an analysis to better identify the obstacles that hinder this practice. However, the valorization, resources and local products through the local assets were found to be beset by certain problems. The inventory of activities of fruit and vegetable in southern Algeria has shown that despite the different products available locally, the need for drying devices that better meet users’ expectations remains very important. It is therefore very appropriate to develop drying facilities that can support local agricultural production and ensure its preservation during peak production. Reflection on the positioning of this practice has proved to be an important step that will reduce the costs of setting up new units.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".