Change Detection of Forest and Habitat Resources from 1973 to 2001 in Bach Ma National Park, Vietnam, Using Remote Sensing Imagery
Bibliographic record
Abstract
SUMMARY Land cover changes have not been well documented in Vietnam. This paper presents new information relevant to land cover modifications and to resource inventory such as forest management and wildlife habitats. Formed in 1991, Bach Ma National Park and its buffer zone is one of the richest regions for biodiversity in Asia, providing habitat for endangered species. The paper assesses the major forest cover changes using Remote Sensing Imagery (Landsat: MSS, TM, +ETM) between the years prior to the establishment of national park status and the years following. Normalized Difference Vegetation Index (NDVI) was used across sensors; for the study area five regions were identified where major land-cover changes have occurred. Between 1973 and 2001 it is estimated that approximately 45 % of the buffer zone was modified, or lost its forest cover, with most changes occurring around 1989, just prior to the park establishment. These changes can most likely be attributed to forest and resource extrapolation that coincided with a high human population density and is supported by extensive road building in the surrounding region. More research is needed to improve presented approaches in order to better safeguard forested landscapes in Vietnam.
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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 | 0.000 | 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".