Analysis of Poaching Activities in Kainji Lake National Park of Nigeria
Bibliographic record
Abstract
Analysis of poaching activities in Kanji Lake National Park (KLNP) of Nigeria was conducted with the aim of investigating the forms and trend of encroachment experienced in the premier protected area, and to determine the locations where poaching occur. Data for the study were collected using two sets of structured questionnaires and secondary data obtained from administrative records. A set of structured questionnaires was administered randomly to 30% of households in ten selected communities close to the park. The second set of questionnaires was administered to 30% of the staff in park protection section of KLNP. In all 403 households and 53 staff members were sampled. Data on poaching arrest were obtained from administrative records. Data collected were analysed using descriptive statistics in form of frequencies of count, percentages, graphs, bar chart and pie chart. Grazing of livestock and hunting were the form of encroachment most arrested in the park between 2001 and 2009. Poachers were most attracted in the park by Animals (92.06%), fuel wood (82.13%), Herbs (73.95%), and Fish (73.95%). Between 1995 and 2009 KLNP recorded the highest arrest (372) of poachers in 1999. Increase in the number of staff of KLNP had no significant effect in the number of poachers arrested within this period. Oli and Ibbi were respectively ranked first (69.98%) and second (45.91%) by household respondents as major areas of poaching. About 52.11% of households are optimistic that poaching can be stopped while 39.5% perceived that it can only be minimized. However, 39.15% of household respondents suggested creation of employment opportunities for households as a strategy that can stop poaching in KLNP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".