Impact of a Climate Smart Technology: Case of the Farmer Managed Natural Regeneration on Trees Biodiversity in Sudano-Sahelian Parklands of Kayes and Koulikoro, in Mali
Bibliographic record
Abstract
In the Sahel, parklands are degraded due to climate change, human and animal pressure. The objective of this research was to assess the effect of Farmer Managed Natural Regeneration (FMNR) on trees biodiversity in Sudano-sahelian parklands of Kayes and Koulikoro, in Mali. Trees inventory was carried out in agricultural areas. The size of an inventory plot was 2500m². The inventory plots were 500 meters apart. The total area inventoried was 20 hectares. The woody flora of studied areas was made of 27 species of trees. Most represented families were Combretaceae (6 species); Mimosaceae (4 species) and Cesalpiniaceae (3 species). Families, species and genera of trees have varied according to the rural communities. In fact 8, 15, 12 and 11 species have been identified respectively in Tieneguebougou; Farako; Guemou and Bienkolobougou. The characteristic species has varied depending on studied areas with 2, 7, 3 and 3 species in respective localities. The index of regularity showed an identical high level of trees organization in Tieneguebougou, Guemou and Bienkolobougou. But, the index was low in Farako. A comparison of indexes values showed although the floristic composition was low in Tieneguebougou; Guemou and Bienkolobougou, their woody stand organization was further constant. The FMNR has increased the biodiversity of trees and consequently the volume of wood in agrarian areas. This biodiversity of trees contributes to carbon sequestration and strengthens the resilience of populations by providing them with goods (firewood, honey, etc.) and eco-systemic services. FMNR is a climate-smart technology, whose scaling up in Mali is essential.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".