The Use of Remotely Sensed Data in Rapid Rural Assessment
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 article discusses how analysis of remotely sensed data can be applied in rapid rural assessment and how its application can expand the spatial analysis of land-use/land-cover (LULC) change. It describes the methodological steps to carry out an LULC analysis based on Landsat Thematic Mapper image analysis under time and budget constraints. The article presents intra-and intercommunity comparisons of different LULC patterns. The discussion focuses on the trade-off between the desirable degree of land-cover class complexity, the level of class detail, and the required ground-truthing associated with each of these choices. The authors conclude that remotely sensed analysis can enhance short-term, low-budget fieldwork. Analysis of remotely sensed data can reduce costs before fieldwork by helping to inform where to concentrate data collection efforts, during fieldwork by extending spatial analysis to areas where accessibility is poor and that otherwise would not be included, and after fieldwork by improving the spatial and temporal scope of the analysis.
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 | 0.000 |
| 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 it