Smart Buff Manager: A Co-Designed Mobile Application for Enhancing Buffalo Farm Management in Thailand
Bibliographic record
Abstract
This study focuses on the co-design and development of a smartphone application, the "Smart Buff Manager," especially for smallholder buffalo farmers in Thailand. The application aims to meet the needs of buffalo farmers by providing an effective and user-friendly tool for record-keeping and farm management. We employed the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) instructional design method to ensure the application addressed the practical needs and preferences of the farmers. The primary features of the app include record-keeping, health monitoring, and breeding management, focusing on various issues faced by smallholder farmers. The user-centered design method provides an intuitive interface that gathers feedback from users, resulting in increased satisfaction and a higher tendency to recommend apps. Implementation of the app significantly improved farm management by enhancing operational efficiency, productivity, and animal welfare. Users of the application reported much greater compliance with vaccination schedules than non-users, potentially improving herd health and long-term production. This case study confirms the potential of mobile technology to improve farm operations and decision-making while also highlighting the essential role of involving end-users in the design process to develop appropriate and effective digital farming systems.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".