Management Effectiveness and Potential for Tourism of Peri-Urban Lusaka National Park, Zambia: A Preliminary Assessment
Bibliographic record
Abstract
Management effectiveness of a park is multi-faceted subject with implications on various aspects of its existence. Determination of the management effectiveness of a protected area is often linked to monitoring processes. Wildlife monitoring is a critical component of wildlife management and integral part of a research programme for Lusaka National Park (49.76 km2). A preliminary study was undertaken to determine the protected area management effectiveness, initially by ascertaining the status and distribution of mega-fauna resources. This was followed by evaluating whether the park management was effective by using status of wildlife populations as surrogate in comparison to initial wildlife stocks. Helicopter and ground line transects, historical data and field patrol data were used for analyses of park’s management effectiveness and potential for ecotourism. Though the study has locally relevant findings, insights on persistence factors such as selection of translocated wildlife, resource ecology and management can benefit park ecologists, managers and other stakeholders especially those responsible for smaller parks of less than 100 km2. However, further research is recommended on wider management effectiveness elements to understand factors affecting the park’s management effectiveness.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".