Land Cover Change Detection Using MSS and MODIS Data: A Case Study for Liangshan-Xiangling Region in Southwestern China
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
As a result of rapid socioeconomic development and climate change, the land cover has been changing in the mountainous areas in southwestern China while the associated ecological environment has been seriously disturbed. This study is to quantify the land cover change in Liangshan-Xiangling Region in Sichuan Province in China from the 1970s to present and to compare the land cover change rates among different land cover types. Two groups of remote sensing data, including MSS data in 1974-1980 and MODIS data in 2002-2007, were utilized to investigate the land cover changes during different time periods in the study area. The NDVI differencing and unsupervised classification compassion methods were used to detect the land cover quality and quantity changes. The results showed that the vegetation cover in the study area decreased significantly in the 1970s, but increased in recent years due to the establishment of nature reserves and enhancement of environment protection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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 it