Foreword to the Special Issue on Digital Innovations in Agriculture Research and Applications
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
The articles in this special section focus on the use of digital technology in agricultural research and applications. Sustainability of global agricultural and food systems is one of the prominent factors for peaceful future of the world in next few decades. Although agriculture is the main part of the global food supply chain, it is under rising pressure of global climate change, environmental deterioration, and falling per-capita arable land. Efficiency and sustainability management at all levels of agricultural planning and production appear as the most promising balancing factor for the short and medium terms. For this reason, timely and accurate information about the current conditions and future predictions in agriculture and the related resources become more important than ever. We are living in an age that the annual production number of transistors in microprocessors is more than the number of wheat grains produced in the same year. This is an indication of increasing data processing capability and decreasing cost. On the other hand, the number of Internet connected devices is estimated to be more than 20 billion now and is increasing rapidly, and Intent-of-Things (IoT) devices have the highest share in this rising trend. While it may be not able to solve long-term global food sustainability issue, the rapid increase in data collection and processing capabilities for agricultural monitoring and prediction may remediate the issue at least in short and middle terms.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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