Connected and autonomous electric and fuel-cell powered agricultural power units: A feasibility study.
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
Agricultural labour shortages coupled with a required increase in global food production and increasingly stringent sustainable farming legislation are creating a ‘perfect storm’ opportunity for a much greater reliance on electric and autonomous technologies in agriculture. Fuel cell (FC), electric vehicle (EV), and connected and autonomous vehicle (CAV) technologies are being successfully adapted to meet the needs of several on-road and off-road vehicular applications. In this article, we focus on the feasibility of integrating FC, EV, and CAV technologies to power units adapted to the autonomous completion of agricultural field operations. Such small-scale autonomous agricultural power units (AAPU) would be intended for cluster/fleet operations and feature communication capabilities facilitated through a next-generation network infrastructure. These AAPUs would be compatible with a variety of agricultural implements to provide operational versatility and value to a wide range of farming operations. Such FC & EV powered AAPUs could reduce lifecycle greenhouse gas (GHG) emissions from agricultural operations by an average of 70% relative to emissions from diesel power units. This article further demonstrates that these autonomous technologies could be leveraged at a cost comparable to current diesel operations in agriculture.
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 | 0.001 |
| 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