Integrating Remote Sensing Data And Rapid Appraisals For Land-Cover Change Analyses In Uganda
Bibliographic record
Abstract
Abstract Rapid population growth, unsustainable land use, and a pervasively degrading landscape are components of a dominant paradigm regarding African development. While recent work articulating the ‘misreading’ of the African landscape have begun to challenge this paradigm, much work remains regarding the pervasiveness and character of this misread. A method is presented for investigating mechanisms of land‐cover change that combines remotely sensed data, archival data, and rapid appraisals in a way less influenced by dominant paradigms. We present a case where increasing human activity is resulting in accumulation of woody biomass on edaphic grasslands of a forest–grassland mosaic, rather than the expansion of grasslands at the expense of forests as is currently understood in that area. These increases in biomass are stimulated by anthropogenic influences that are shaped by institutional and edaphic factors. We do not claim that resources are being pervasively enhanced across sub‐Saharan Africa under conditions of population growth, but that there may be many mechanisms of change, resulting in both degradation and enhancement, occurring simultaneously across sub‐Saharan Africa or even intra‐regionally within a nation under these conditions. The integration and application of these methods serve to improve applied analyses of land‐cover change to better characterize these mechanisms, and avoid the wrong policy prescriptions. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".