In Niger, the Expected Futur Climate Will Provide Better Conditions than the Current One to Diospyros Mespiliformis Hochst. ex A.DC. Rich.
Bibliographic record
Abstract
The human use of plant resources and land to face increasing population needs in Africa to the regression or even the disappearance of some useful multi-purpose species such as Diospyros mespiliformis Hochst. Ex A. Rich. Increasing climatic variability is an additional threat for these species. The present study aims to identify the areas that are potentially favorable to D. mespiliformis conservation or regeneration in Niger and to analyze the impact of the current climate change. Thus to assess the D. mespiliformis distribution areas, the geographic coordinates of D. mespiliformis, the bioclimatic data, the soil and vegetation cover were collected and used to modeling based on the principle of maximum entropy (MaxEnt). The soil cover, annual cumulated precipitations and the average temperature are the most determining variables. This study also shows that the ecological niche of D. mespiliformis is located in the Central and Eastern bioclimates, within which almost 3% of the surface is very favorable under the current climate conditions and may reach 3. 94 % under 2050 ones after. These results indicate that the climate change expected in Niger is expected to be more favorable to the studied species than the current climate conditions. This represents an opportunity for its domestication.
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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 | 0.000 | 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) | 0.002 | 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".