An Assessment of the Takamanda-Mone Landscape as a Potential Forest Habitat for the African Forest Elephant (Loxodonta africana cyclotis) in the South West Region of Cameroon
Bibliographic record
Abstract
The Humid Forest Zones (HFZ) of Central Africa, have relatively been understudied in the area of their potentials as large mammals habitats. This is particularly true for forest elephants which are predominantly migratory across large ecological landscapes. This migratory phenomenon makes it difficult to state with certainty their status and habitat need in time and space that can help conservationist and policy makers to designate /classify a landscape as a large mammal (elephant) habitat. It was from this backdrop of uncertainty that this study on the Assessment of the Takamanda-Mone landscape as a potential forest habitat for the African forest elephant (Loxodonta africana cyclotis) in the South West Region of Cameroon was consecrated. The study in order to achieve set objectives, made use of Socio-economic surveys that involved the use of questionnaire, some selected Participatory Rural Appraisal (PRA) tools. Biological assessment techniques such as transects, poacher guided survey as well as the use of the Geographic Information System (GIS), techniques were employed for data collection and analysis. Results revealed that the Takamanda-Mone landscape is a potential forest habitat for the African forest elephant (Loxodonta africana cyclotis). Their presence and migration in and out of the study area is known to be affected by the two seasons (dry and wet) that prevail in the areas. Their presence was found to be more marked in the wet and low in the dry season. Results further revealed that evidences of the African forest elephants (Loxodonta africana cyclotis) presence in the study area ranged from physical encounters and observation of the animals, left over of crops destroyed, recorded vocalisations of the animals far-off villages, forest fruits devastation, fresh and old forest tracks, footprints, feeding signs, debarking and felling of trees to dung droppings. The evidence of direct encounter and observing the animals was strongly associated with the older population (40 years and above). Result also showed that during some of the direct encounters with the population, the African forest elephants were known to have killed about 10 persons and injured about 17persons over the past 25 years in the study area. Conclusively, the study area is a habitat for the African Forest elephant (Loxodonta africana cyclotis) and is being recommended to be designated as a habitat for the protection of species and other wildlife 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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".