P2P Key-Value Storage Synchronization Workflow for Agronomic Data Management
Bibliographic record
Abstract
The use of mobile devices such as smartphones, tablets, smart watches and notebooks in the agriculture sector is gaining significant popularity. Through mobile technologies, farmers are aided to quickly and easily communicate, advertise goods and services, as well as accessing agronomic data in soft-real time. Though mobile devices are a good source of agronomic information access and dissemination, the over-dependence on wireless communication protocols for communication is a bit of a challenge. Due to the mobility of farmers, the wireless networks can experience bandwidth fluctuations and that can hamper data transfer and management in mobile-server (cloud) ecosystems. To address this issue, previous works propose mobile data storage to support information access in an offline mode. However, the question of how to efficiently management the data state on the mobile is under-studied. In this work, we proposed a mobile-cloud architecture that enables farmers to manage data transfer and storage of agronomic data on their mobile devices in the face of the network challenges. Three different P2P Key-Value Storage methodologies are presented which are: 1) Bloom Filters algorithm, 2) whole state data transfer, and 3) exchange of delta (updates) only. The whole state data transfer is only recommended when there is stable wireless connection.
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.
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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".