Modelling the current and future distribution of <i>Caesalpinia bonduc</i> (L.) Roxb: Its implication for future conservation of the species in the Southern Benin
Bibliographic record
Abstract
Abstract Caesalpinia bonduc (L.) Roxb, the most commercialised medicinal species in Southern Benin, is reported to be extinct in the wild due to anthropogenic pressures on its natural habitats. Remaining individuals can only be found in traditional agroforestry systems and home gardens. It is therefore important to understand how spatio‐temporal distribution of the species could be impacted by changing environmental conditions and propose strategies to be used for its conservation. Maximum Entropy (MaxEnt) modelling technique was used for modelling the current and future distribution of the species using present‐day combined with two future forecast scenarios (Representative Concentration Pathways‐RCP): low‐RCP4.5 and high‐RCP8.5 emission scenarios. Environmental and demographic factors have impacted the distribution of the species. Rainfall driest quarter (44.5%), population density (30.4%), rainfall driest month (14.7%), potential evapotranspiration (6.5%) and number of dry months (4%) mostly contributed to the model. High suitable areas of the species will increase about (2.28%) and (0.06%) with the RCP8.5 and RCP4.5, respectively, at horizon 2055. In contrast, less suitable areas will decrease about 3.11% (RCP4.5) and 1.71% (RCP8.5). In‐situ conservation strategy is suggested to restore the species in the wild taking into account suitable areas for its growth, development and reproduction. Circa‐situ conservation should be promoted in agroforestry systems and home gardens for sustainable use of the species.
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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 | 0.000 |
| 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".