Mapping irrigated agriculture in fragmented landscapes of sub-Saharan Africa: An examination of algorithm and composite length effectiveness
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
Accurately identifying irrigated areas is crucial for sustainable development, food security, and effective land and water resource management. However, incomplete or outdated national estimates of irrigated areas underestimate the extent of it, particularly among smallholders. This study aimed to address this issue by investigating the impact of different algorithms and composite lengths on predicting irrigated agriculture in four study areas in Mozambique. The study found that the choice of algorithm and composite length notably impacted the accuracy of identifying irrigation. Shorter composite lengths, such as 2-monthly or 3-monthly composites, were more effective in identifying irrigation in fragmented and dynamic landscapes, while longer composite lengths were better suited to stable classes and homogeneous landscapes. Artificial neural networks, support vector machines, and random forests were all effective algorithms for classifying irrigation. However, the study emphasised the importance of considering hotspots and agreement maps when identifying irrigation. Agreement maps combine the classification results of multiple models, providing better insights into the core areas of irrigated agriculture and allowing for a better understanding of irrigation dynamics and policy decision-making, particularly among smallholder systems. This research provides valuable insights for those working on remote sensing-based irrigation mapping and monitoring in sub-Saharan Africa, focusing on identifying smallholder irrigation with greater certainty.
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 | 0.000 | 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