Remote sensing of land cover change dynamics in mountainous catchments and semi-arid environments: a review
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
This paper investigates the impacts and dynamics of land use/land cover (LULC) change dynamics in mountainous catchments in semi-arid regions, focusing on drivers, methods, and hydrological impacts. This study reviews studies using the application of remotely sensed data and spatially modified data, highlighting advancements in LULC assessments through GIS integration and predictive modelling. Key drivers include agricultural expansion, population growth, urbanisation, and infrastructure development, which transform forests and grasslands into built environments, affecting ecosystem services and biodiversity. LULC changes significantly impact hydrology, leading to increased surface runoff, poor water quality, and disruptions in the hydrological cycle. Agricultural expansion also contributes to habitat fragmentation ad biodiversity loss. This study underscores the importance of sustainable land management and informed policy decisions to mitigate negative impacts and enhance ecological resilience in semi-arid regions.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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