Variabilité Climatique Et Changements Dans L’environnement À Korhogo En Côte D’ivoire : Mythes Ou Réalité ?
Bibliographic record
Abstract
Climate change which is considered as a threat to biodiversity, causes paradoxical situations everywhere it manifests itself. These include, among others, drought and desertification; floods and storms; sea level rise, reduction of the vegetation cover; water supply sources and, animal and plant species disappearance. In Korhogo, in the North of Côte d’Ivoire, the climate variability and environmental change phenomena are perceived and described by the population. A qualitative approach with historical and comparative analysis tools are used in this study to focus on the perceptions of the populations through indicators of both climate variability and environmental changes. It also analyses the major challenges the population will face, which are mainly related to alteration in farming practices and seasons, loss of production, and extension of the lean period, the risk of food insecurity and conflicts related to the exploitation and the distribution of water resources; and mostly, to the lack of information- educationcommunication on the phenomenon. On the whole, although the study brings out an awareness about climate variability and environmental changes, it especially show the need for emergent multidimensional actions for effective response strategies to the changes observed in the area.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".