Quantifying Ecological, Economic, Social, and Governance Attributes for Urban Forest Eco-Tourism Using MDS-RAPFISH Approach
Bibliographic record
Abstract
Urban forests play a crucial role in ecological conservation and environmental preservation in urban environments.Their sustainability is vital given the mounting ecological and environmental pressures they face.This study aims to identify factors influencing the sustainability of forest management in terms of ecological conservation and eco-tourism, with a focus on Dumai's urban forest.We employed a Multidimensional Scaling (MDS) approach using the RAPFISH (Rapid Assessment Technique for Fisheries) program for our analysis.The results indicate that the Dumai forest ecosystem falls into the moderate category, while its sustainability level is considered less sustainable across four dimensions: ecological, social, economic, and governance.Leverage analysis identified several sensitive attributes for the sustainability of forest management, including three ecological attributes (vegetal diversity, tree species density, animal diversity), four economic attributes (job and business opportunities, multiplier effect, non-tax revenue, community income), five social attributes (level of education, society participation, conflict, community perception, public communications), and four governance attributes (regional information database, monitoring and evaluation system, human resources, infrastructure).These findings underscore the necessity of balancing the ecological and socio-economic functions of the Dumai urban forest for ecological conservation and eco-tourism.By employing the Multidimensional Scaling (MDS) approach, this study offers new insights into the comprehensive understanding of the factors affecting the sustainability of forest management practices, and their impacts on ecological conservation and eco-tourism within an urban context.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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".