Assessment of Tree Planting Efforts in Lagos Island Local Government Area of Lagos State, Nigeria
Bibliographic record
Abstract
Investigations were carried out to review tree planting activities within the Lagos Island Local Government Area of Nigeria. The city is a center of commercial activity within a hot tropical environment. Efforts have been made by both governmental and private bodies to promote tree planting within the area in mitigating the effects of urbanization on the environment particularly in the area of climate change. However, it became necessary to assess the tree planting activities so as to properly place its achievements and positive contributions to the environment. The review is also to highlight the areas where more efforts are needed. An enumeration of existing trees was carried out with the aim of assessing the distribution, specie types and density of coverage. A handheld GPS device was used to acquire the coordinates of trees which were then mapped. Further analysis using GIS was done. Interviews with tree planting officials and public volunteers were also carried out. A total of 293 trees was identified within the study area which is about 8.7 km2 in size. The result indicates a paucity of trees in the area despite the various tree planting efforts. A high mortality rate of trees was observed. Further findings indicated that the public’s desire to support, manage and maintain the planted trees was poor. It was observed that the tree planting activities were seen as a curse rather than a blessing by market men and women within the study area. This study suggests more public enlightenment and that edible species should be planted in place of exotic ones being used.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".