Impact of land use change on ecosystem services of southwest coastal Bangladesh
Bibliographic record
Abstract
This study assessed impact of land use change on ecosystem services (ES) of the southwest coastal Bangladesh, by combining Landsat data and published value coefficients of different ecosystems. Land use categories were estimated using satellite images from 1980 – 2016. Changes in the value of ES delivered by each of the land use categories were estimated from respective value coefficients. Results revealed that agriculture land decreased by 253,928 ha and aquaculture land increased by 272,032 ha within 1980 – 2016. Meanwhile, the total value of ES decreased from US$ 90.45 to 88.22 billion. Decline of agriculture was the largest contributor (US$ 1.41 billion) to the loss of ES, followed by deforestation (US$ 0.94 billion). Forest is the major contributor to the ES of this region and could largely impact on the ES value. Future land use policy could be targeted to promote sustainable agriculture and conservation of forest.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".