Trend Analysis of Mangrove Forest Health in East Lampung Regency as Community Preparedness for Natural Disasters
Bibliographic record
Abstract
The condition of mangrove forests in East Lampung Regency is currently experiencing degradation, resulting in the function of mangrove forests being reduced, especially in preventing natural disasters. This research is the third-year measurement of the activities of monitoring the health of mangrove forests in East Lampung Regency. The purpose of this study was to determine the value and trend category of the health condition of the mangrove forest in East Lampung Regency. The stages of this research, namely: measuring the health trend of mangrove forests in the six FHM clusters that have been built and analysing the data using the Forest Health Assessment Information System software. The results of this study indicate that the trend value of the health condition of mangrove forests in each location is Kuala Penet (8.64-20.90) with a good category because CL-1 and CL-2 are constant, Margasari (2.52-5.57) in the bad category because CL-3 and CL-4 decreased, and Purworejo (5.58-8.63) in the moderate category because CL-5 and CL-6 increased. Thus, the average trend value of the health condition of the mangrove forest in East Lampung Regency (7.70) is in the moderate category. This can provide information to the community on preparedness in dealing with natural disasters.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".