Change of land Use / Cover in Algeria's Steppe Ecosystem: A case Study of Southern Hodna
Bibliographic record
Abstract
This study employs a change detection analysis of land use/cover in the Algerian steppe ecosystem of Southern Hodna over the span of 21 years, with the integration of the Geographic Information System (GIS) and Remote Sensing. Two Landsat satellite images (1995 and 2016) were classified via supervised classification into four major different classes: agriculture, rangeland, degraded rangeland, and sand dunes. After that, the post-classification change detection technique was used to analyse changes through cross-tabulation. The results show a notable growth in the agriculture class by 8.45% due to agricultural development in this region, with irrigation using groundwater. Sand dunes and degraded rangeland also increased by 6.94% and 23.48% respectively. A huge loss in the rangeland class by 38.87% was noted due to the continuous expansion of agriculture at its expense and non-stop overgrazing. This study mainly demonstrates the importance of spatio-temporal change detection in disclosing change in land use/cover that occurred in Southern Hodna, along with the necessity to develop strategies to further reduce the land degradation that this region faces.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".