Bibliographic record
Abstract
The diversity of the mobile landscape is shaping the business outlook of several enterprises including the agricultural sector. Today, there are several opportunities that can be harnessed in terms of productivity, mechanization, revenue, and so on when mobile technology is combined with agriculture technology. Especially, with mobile apps, agriculturists such as crop farmers can have access to timely information that can aid them in making decisions on pesticide information control. The problem however is that, mobile devices communicate over wireless mediums that can be unreliable. This situation causes the back-end to be unreachable from the mobile, and subsequently denial of service when the farmers want to access information at the crop field. This work proposes a reliable distributed mobile architecture that enables crop farmers to access timely information. Firstly, we designed a three layered architecture that comprises a middleware to enable the farmers to access the data when they have access to Wi-Fi or 3.5/4G. Secondly, the mobile node is designed to hoard the data in a NoSQL database to prevent frequent requests being sent to the back-end and to ensure offline accessibility. Finally, when the farmers are offline, they are facilitated to communicate with their friends through Bluetooth in order to synchronize the most up to date information. Preliminary tests show that the proposed system is reliable in terms of the management of the mobile data.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".