Harnessing Electric Tractors for Sustainable Farming in Ontario
Bibliographic record
Abstract
In the last few decades, there have been growing concerns about climate change and its effects on humans, plants, and animals. The climate emergency impacts the health and economies of all countries and people around the globe; however, it is impacting people in low-income brackets at a much greater rate (ironically, low-income people have contributed the least to create and sustain the problem). Anthropocentric greenhouse (GHG) emissions in general and carbon emissions (C02) are primarily responsible for climate change. Therefore, the immediate reduction of all those emissions is vital to solving the climate crisis. One of the most effective strategies for reducing emissions is shifting to cleaner energy sources and decarbonizing energy use in the agricultural sector (which currently is one of the big users of fossil fuels). Therefore, a key goal of this major research report is to explore the advantages and barriers associated with using electric tractors to improve rural life while tackling pollution and reducing carbon emissions in rural areas. This research adopts a case study methodology to critically analyse the viability of introducing electric tractors powered with renewable energy, such as solar photovoltaics, which can help address the climate crisis while creating tangible benefits for Ontario farmers. Data was collected from electric tractor manufacturers in agricultural sectors across Europe and North America to understand market structure and environmental benefits of transitioning to electric mobility solutions powered by renewable energy sources in the agricultural sector of Ontario.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".