Multiple Land Use Benefits of Peri-urban Forest (Arakanga Forest Reserve, Ogun State, Nigeria): Perception of Resource Users and its Implication
Bibliographic record
Abstract
The study examined multiple land use benefits in a peri-urban forest in Abeokuta, Ogun State, Nigeria and the perception of resource users on the significance of the benefits. Stratified Random Sampling technique was used for the study with 100 respondents selected. The settlement was divided into four strata based on existing pattern in the area. The four strata are Ajegule, Ibode Olude, Ilugun Titun and Mawuko. From each stratum, twenty five respondents were randomly selected with a total of 100 respondents from the study area. Questionnaire and interview were used as the instrument of data collection. The benefits derived from the reserve cut across all age groups, ethnic background, educational levels and marital status of respondents. The forest reserve provides multiple benefits in terms of goods and services and environmental protection. Mantel measurement for combined benefits showed firewood as the most dominant with indispensible value of 34 followed by Teak leaves collection 24 and 16 for geological material extraction. The major objective of the reserve is timber and poles production but simultaneously other multiple benefits such as firewood, snails, teak leaves, medicinal plants, bushmeat and geological materials were derived from the reserve. Consequently, the forest reserve contributes to livelihoods of the surrounding communities. This was measured through perception of the respondents with positive mean values and standard deviation of Likert rating. It is therefore recommended that increased conservation effort must be ensured through appropriate forest policy formulation along with the introduction of alternative domestic energy source to firewood to enable the forest contribute more to the welfare of surrounding communities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".