Plant diversity and carbon storage in roadside trees in Cotonou (Republic of Benin)
Bibliographic record
Abstract
The purpose of this paper was to assess the diversity of roadside trees and their capacity in the storage of carbon in Cotonou. This study has been posited as a prerequisite for urban greening management for the mitigation of the ongoing global warming. Purposive sampling was used involving four (04) Avenues, namely Canada, Proche, Missebo-Zono and Marina, selected using the cartographic data from the DST (Direction des Services Techniques). The sampling was carried out using the sum of distances planted with alignment trees on the four investigated Avenues. Plant diversity was analyzed while dendrometric and plant community structure were characterized. The floristic inventory on the roadside trees of the four (04) avenues reported a total of 1075 plants belonging to 12 species, 11 genera and 9 families. The most abundant tree species was Khaya senegalensis (67.82 %), followed by Terminalia mentaly (21.15 %). The least represented species was Roystonea regia (2.01 %) found only on the Marina Avenue. Average Shannon Diversity Index and Pielou evenness values were respectively 1.93 bit and 0.55 indicating a weak diversity of tree species along the four Avenues. Noticeable high difference was observed in the floristic composition on the four Avenues regarding species, genera and family richness. Marina (9 species, 8 genera and 7 families) appeared to be the most diversified while Proche (3 species, 3 genera and 3 families) was the least. Non native tree species were the most dominant. Stored C mean in these roadside trees ranged between 5,877.8 tons C and 6,311 tons C, corresponding to the values of 744.03 kg C/ha and 798.87 kg C /ha. These results provided an evidence of the purification role of trees and posit urban forest as a solution to be encouraged to the ongoing pollution of the environment in cities. Keywords: Urban forestry, roadside trees, carbon stock, Cotonou, Benin
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".