Development of a Sustainable Internet of Things-Based System for Monitoring Cattle Health and Location with Web and Mobile Application Feedback
Bibliographic record
Abstract
Cattle farming is undoubtedly one of the most lucrative subsectors of agriculture globally, but faces significant challenges such as the inability to monitor cattle health and location, and cattle rustling. This research aimed to develop a system to resolve these issues using sensors to monitor ambient/body temperature, magnetometer, and GPS. The proposed system comprises these components in a head strap. Data were transmitted via a long-range (LoRa) module to a base station, then to a website and mobile app using General Packet Radio Service (GPRS)/satellite. Information was received and monitored in real-time. Testing showed the system could be deployed in vast farmland to monitor cattle health and location satisfactorily in real-time. Unlike other systems, this system monitors cattle health and location with/without mobile network coverage due to satellite communication. In conclusion, the proposed system monitors cattle health and location status with or without mobile network coverage due to an alternative communication channel (satellite) compared to other related systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".