Mangrove Spatial Distribution in the Indian Sundarbans: Predicting Salinity-Induced Migration in a Changing Climate
Bibliographic record
Abstract
This study contributes to understanding the physical and economic impacts of progressive, climate-driven aquatic salinization on the spatial distribution of mangrove species in the Indian Sundarbans, which accounts for about two-fifths of the 10,200 km2 tidal-wetland forest delta. To estimate future mangrove distribution, a five-step analysis was undertaken, using high-resolution spatial assessments. A current (2015) basemap and overlays of salinity tolerance for major mangrove species and their assemblages and projected location-specific aquatic salinity for 2050 were used to predict salinity-induced migration. The results show gain-and-loss patterns, with salt-tolerant species predominating at the expense of freshwater species. These changes are likely to reduce the flow of ecosystem services, adversely affecting the livelihood options of poor people in adjacent areas. Effective management will require establishing baseline data for monitoring system changes over time, protocols for maintaining species health, and support for mangrove regeneration and restoration. Resources should also be directed to alternative livelihoods for mangrove-dependent households. The study recommends an integrated policy approach, focused on rising salinity, changes in mangrove dynamics, and the welfare of mangrove-dependent communities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".