Socio-Spatial Dynamics of Land in Southwest Niger: The Case of the Commune of Gothèye
Bibliographic record
Abstract
In south-west Niger, ecosystems are losing several hectares of their surface area every year due to internally displaced persons and refugees. The commune of Gothèye is not immune to this situation. The aim of this research is to assess the impact of displaced persons and refugees on socio-spatio-temporal dynamics of ecosystems using Landsat images. To achieve this, Landsat TM, Landsat ETM+ and OLI 8 satellite images from September and March were used (2010 to 2024). Operations on Envi 5.3, field validation output and finally mapping on ArcGIS were the steps involved. Discrimination is significant, with kappa coefficients of 0.97, 0.96, 0.86 and 0.85. The results obtained indicate a degradation of natural ecosystems, reflected in a change in landscape structure, with a marked reduction in the quantity and quality of ecosystem goods. Analysis of the evolution of land use showed that 31% of the land remained in its initial state (unchanged), 69% underwent modifications, and 11% was converted to cropland. Over these fourteen years, the study area has undergone changes in land use patterns, which have resulted in a modification of landscape structure, with a marked decline in the quantity and quality of ecosystem services.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".