Pola Keterkaitan Perubahan Distribusi Kepadatan Vegetasi dengan Penggunaan Lahan DAS Pesisir, Case Study : DAS Garang
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
The diversity and density of vegetation is key in managing sedimentation throughout the watershed, especially in the central and downstream regions. Measures of vegetation diversity and density that can be used are the Normalized Difference Vegetation Index (NDVI) which is a measure of vegetation greenness (chlorophyll levels), Normalized Difference Water Index (NDVI) to measure vegetation wetness levels, and soil adjusted vegetation index (SAVI) for low canopy vegetation cover. For watersheds that stretch long to downstream or coastal areas, the measure of land diversity needs to be supplemented by using the Normalized Difference Built-Up Index (NDBI) indicator that is useful for land use planning. Information on this vegetation index can be obtained by processing a satellite imagery map. The benefits of processing vegetation indexes in watershed management are becoming increasingly important in the era of climate change, especially with regard to efforts to harmonization of relationships between environmental elements that include the diversity of the interests of the resident population. That's why this research was done in order to find the link between natural factors including the impacts of climate change and humans. The test began by looking for changes in the NDVI, NDWI, SAVI, and NDBI indexes from the last 5 years with the Global Indicator Spatial Association (GISA) analysis using Moran's I Global Index, followedby looking for the association with the pattern of population change and the percentage of the area of the settlement. The results showed a link between moran's I Global index change pattern of population change and the spread of residential developments. The tendency of the distribution of this settlement area is an important point in analyzing the influence of its dispersal patterns in a watershed so that the continued impact of potential erosion and sedimentation triggered will be the main consideration in watershed management.
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 | 0.001 | 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 | 0.001 | 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 it