Connected and autonomous electric and fuel-cell powered agricultural power units: A feasibility study.
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.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".