Bibliographic record
Abstract
Increasing consumer awareness of the environmental impact of food production has prompted interest in locally grown food in Ontario. The research reported here had the objective of quantifying the carbon footprint of Ontario grown wheat. A spreadsheet was developed and populated with data and emission coefficients gathered through consultation of the literature. The spreadsheet expresses the carbon footprint in the life cycle of Ontario wheat in CO2 equivalent (kg CO2). The life cycle of wheat includes production, transportation, the use of machinery and application of agricultural chemicals such as pesticides and fertilizers. Since there are insufficient industrial data of manufacture of machines, they were not included in the calculations. The accuracy of this spreadsheet was examined by comparing its results with results of the Agriculture and Agri-Food Canada (AAFC) Greenhouse Gas (GHG) Calculator. The total farm emission of the AAFC GHG Calculator was 3960.2 Mg CO2, while the created spreadsheet had a result of 2963.1 Mg CO2. The spreadsheet has a lower emission than AAFC GHG Calculator because machine manufacture was not included in the spreadsheet. For individual categories agreement was quite close, most categories are within 90% agreement. As a conclusion, results between AAFC GHG Calculator and spreadsheets are similar hence demonstrate the accuracy of the spreadsheet created. Fertilizer production and direct emission from the soil were responsible for 89% of the GHG emissions from Ontario grown wheat.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".