Mangrove plantation suitability mapping by integrating multi criteria decision making geospatial approach and remote sensing data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mangroves are woody plant communities that appear in tropical and subtropical regions, mainly in intertidal zones along the coastlines. Despite their considerable benefits to humans and the surrounding environment, their existence is threatened by anthropogenic activities and natural drivers. Accordingly, it is vital to conduct efficient efforts to increase mangrove plantations by identifying suitable locations. These efforts are required to support conservation and plantation practices and lower the mortality rate of seedlings. Therefore, identifying ecologically potential areas for plantation practices is mandatory to ensure a higher success rate. This study aimed to identify suitable locations for mangrove plantations along the southern coastal frontiers of Hormozgan, Iran. To this end, we applied a hybrid Fuzzy-DEMATEL-ANP (FDANP) model as a Multi-Criteria Decision Making (MCDM) approach to determine the relative importance of different criteria, combined with geospatial and remote sensing data. In this regard, ten relevant sources of environmental criteria, including meteorological, topographical, and geomorphological, were used in the modeling. The statistical evaluation demonstrated the high potential of the developed approach for suitable location identification. Based on the final results, 6.10% and 20.80% of the study area were classified as very-high suitable and very-low suitable areas. The obtained values can elucidate the path for decision-makers and managers for better conservation and plantation planning. Moreover, the utility of charge-free remote sensing data allows cost-effective implementation of such an approach for other regions by interested researchers and governing organizations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it