Correlation Between Participation Variables and Forest Health Parameters in Mangrove Forest Management
Bibliographic record
Abstract
Currently, mangrove forests are experiencing a lot of damage and land degradation, which impacts the forest's health condition.One of the consequences is community activities.This study aims to determine the relationship between community participation based on management stages and the health status of mangrove forests in Margasari Village, East Lampung Regency.The methods in this study are through a quantitative approach, which includes: the calculation of community participation scores using the Likert scale method, assessment of forest health status using Forest Health Monitoring (FHM) techniques, and spearman rank correlation analysis.The results showed that the people of Margasari Village actively participated (high category) in managing mangrove forests.The health of mangrove forests in Margasari has an average score of 5.04 (moderate).Mangrove forests must be followed up, especially in pest and disease maintenance and monitoring activities.Thus, there is a significant real relationship between community participation variables based on the management stages to the health status of mangrove forests.The actual relationship is in the variables of the planning stage, implementation stage, maintenance stage, and evaluation of the tree crown condition parameters with a consecutive correlation coefficient value of 0.418, 0.410, and 0.482 (medium correlation).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".