Conflict-related environmental degradation threatens the success of landscape recovery in some areas in Tigray (Ethiopia)
Bibliographic record
Abstract
Armed conflicts can lead to environmental degradation, thereby threatening the basis of people’s livelihoods and well-being. Identifying areas where conflicts drive environmental degradation is important for designing effective recovery strategies, but this is inherently challenging in insecure contexts. We use a case study in Tigray, Ethiopia to illustrate how open-source satellite data can be used to support the identification of woody vegetation loss during armed conflicts in situations where ground-based assessments are difficult or impossible. Areas of potential woody vegetation loss extend across 930 km2 (approximately 4% of the area occupied by forest and other woody vegetation in Tigray) and appear to be concentrated mostly along major roads; however, vegetation recovery has continued during the war across a significantly larger area (approximately 2600 km2). Spatial patterns of woody vegetation loss appear to be unrelated to drought conditions and large-scale wildfires. Based on these observations and anecdotal evidence of deforestation, we propose that it may be conflict-driven deforestation, caused by increases in fuel wood demands, that are driving the woody vegetation losses in some areas of Tigray. Eventual recovery efforts will have to consider the loss in landscape health during the war in areas where woody vegetation has declined, and include efforts to restore this vegetation to ensure both food security and livelihoods. Open access satellite data, together with ground-based data collection, could inform such post-war restoration efforts by helping identify degraded areas at a regional scale.
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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 | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".