The Critical Level of Mangrove Ecosystem in Lariang Watershed Downstream, West Sulawesi-Indonesia
Bibliographic record
Abstract
Critical land is a land whose soil condition has experienced or is in the process of physical, chemical, or biological damage which ultimately endangers hydrological, orological functions, and agricultural production. This research purposes were to determine the level of criticality of mangrove ecosystems, as the basis for sustainable management. Determination and delineation of the location were carried out photogrammetrically using Landsat 7 ETM + Band 542 imagery and maps, as well as terrestrial by direct measurement in the field. The species inventory and identification, tree/pole potency, saplings and seedlings used the line plot sampling and spot check methods. The results showed that the mangrove ecosystem area was of 577.07 ha, condition of dense (uncritical) vegetation reached an area of 138.16 ha (23.94%), followed by a rare (critical) condition of 286.63 ha (49.67%), while a damaged condition (very critical) 152.28 ha (26.39%). The dominant mangrove species were Sonneratia alba, Rhizophora apiculata, Avicenia marina, and Rhizophora mucronata. The main determinant of the mangrove ecosystems criticality was the mangrove cover area reduction as the non-mangrove land (ponds) impacts. To improve the quality of mangrove forest ecosystems, sustainable conservation is needed, one of which is the preparation of basic mangrove critical data and community empowerment. They are needed to restore, maintain and improve the function of forests and mangrove forest lands in order to increase their carrying capacity, productivity and their role in maintaining life support systems through rehabilitation programs.
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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 | 0.000 |
| 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) | 0.001 | 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".