Deforestation Activities in Ezekoro Forest
Bibliographic record
Abstract
Globally, forests serve as the largest storehouses for (non) indigenous trees, and are essential for the ecosystems’ sustenance, yet, increased deforestation practices associated with activities, such as tree logging, agriculture, and urban expansion continue to put pressure on existing forest areas, leading to massive land use cover change. Ezekoro Forest has been declining at an alarming rate. Detecting the land use cover change, and examining the drivers can assist in informing policies, thereby reducing climate risks since trees are essential in regulating global temperature, rainfall, oxygen, carbon, and environmental protection. They protect against (non) disasters, such as floods, desertification, and erosion. For this chapter, we examined deforestation and environment degrading activities in Ezekoro Forest and their implications for climate change risks in Southeast Nigeria using primary and secondary data sources. Using environmental justice as a lens, we used qualitative methods of field observation, random in-depth interviews, and photographic images with key informants. Also, Geographic Information Systems (GIS), remote sensing and satellite images covering a 20-year period (2001-2021) using Landsat 7 image of 2001 and Landsat 8 image of 2021 was employed. Besides predominating strong winds that have reduced the vegetation cover, we found clear evidence(s) of environmental degrading actions that have reduced the quantity of woody bamboo trees to 30%, heightening erosion and flooding activities and a low crop yield of 20%. About 35% of the harvested bamboo trees and other tree species were majorly cut for fuel wood, construction materials and trading, 30% was used for farming activities, 20% were used for building construction activities, and 10% and 5% were employed for hunting and dumping of refuse, respectively. Findings from satellite image showed drastic changes in the landuse/landcover of Ezekoro Forest. Whereas bare surface indicating deforested (loss of trees and vegetation) areas was 1.3% in 2001, it increased to 23.5% in 2021, a change of 22.2%. Similarly, built-up area was 4.4% in 2001 but rose to 30.1%, a change of 25.7%. However, vegetation cover was 94.3% in 2001 but decreased tremendously to 46.4%, a change of 47.8%. This is an indication of intense deforestation over the years. In lieu of environmental justice and social change, it is critical to aim for environment-climate based actions, such as participatory action research and inclusion of women in forest governance through appropriate forest development structures, to enable co-production of local and scientific climate-related knowledge.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".