Socio-economic Impact of Seasonal Wildfire on Forest and Wildlife Biodiversity in Ogun State
Bibliographic record
Abstract
The paper examined Socio-economic impact of seasonal wildfire on biodiversity in Ogun State, especially in plantations and farmlands. Wildfire destroys ecosystems with loss of habitat and genetic resources. However, wildfire generally has historical record of its use as a tool by early man for clearing forest for agriculture to increase food production due to rising population. The forest is cleared and burnt despite providing array of goods and invaluable ecological services. The forest provides habitat for wildlife and once destroyed the fauna resources is equally destroyed. Consequently, if wildfire is prevented man stand to gain a lot from the conservation effort especially on genetic resources that will be protected within the forest. An evaluation of losses revealed that 70% of Nigeria's tree population are lost annually to forest fire with forest crop valued at over 19 million. Yet the sources of fires have not changed- natural and artificial. The artificial source resulting from human activities towards economic emancipation remained the major source of concern with rising population and increasing demand for food. Field experiments at Forestry research Institute of Nigeria showed that both early and late burning have effect on tree population with late burning having major effect on tree population. Late burning recorded 54% survival for trees, early burning 69.8% and protected plot recorded 73.8% survival. This indicates that wildfire should be prevented from all ecosystems and vegetation. Animal species however recorded highest number of 22 in farmland after using fire as a tool in land preparation with secondary regrowth recording the least population of 107. The effect of fire on plants and animals therefore underscore the need for prevention of wildfire and conservation to reduce ecosystem destruction, animal susceptibility to diseases, wildlife destruction and consequent genetic erosion of biodiversity in Ogun State. It is therefore recommended that policy statement must ensure enforcement and compliance with legislation on wildfire across the state.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".