Economic Damages of Primates on Farmlands in Old Oyo National Park Neighbourhood
Bibliographic record
Abstract
This paper investigated an estimated amount of losses incur due to damages caused by Primates on farmlands and the methods of control in the neighbourhood of Old Oyo National Park, Nigeria. Primary data were collected and used for the study. Data were collected through the use of open-ended questionnaires administered to all the affected farmers in the study area. Non-probability snowballing method was used in locating and sampling affected farmers. The result revealed that an estimated average of ₦3 979.18 ± 5.79, ₦ 3 981.33 ± 3.67, ₦3 974.60 ± 6.85, ₦3 905.85 ± 6.32 worth of Yam tubers were lost by each farmer in Abanla, Imodi, Budo Alhaji and Fomu villages respectively. Also, an estimated average of ₦67 656.35 ± 420.90, ₦68 248.14 ± 500.97, ₦66 094.73 ± 482.22, ₦67 817.90 ± 554.17 worth of maize were lost on farmlands by each farmer at Abanla, Imodi, Budo Alhaji and Fomu villages respectively. In addition, an estimated average of ₦4 780.13 ± 1.53, ₦3 993.09 ± 4.50, ₦5 834.50 ± 4.48, ₦5 321.33 ± 3.99 worth of cassava plants or tubers were lost to primate in the respective villages mentioned. Furthermore, most of the respondents (43.33%, 50%, 39.3% and 46.15% at Abanla, Imodi, Budo Alhaji and Fomu respectively) engaged the use of fire arms in the control of Primates on their farmlands. Results also shows that three basic techniques used in controlling damages by Primates in the study areas are; fire arms, traps and chasing. Recommendations were made based on the outcome of the study.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".