Anthropogenic energy and carbon flows through Canada’s agri-food system: Reframing climate change solutions
Bibliographic record
Abstract
Greenhouse gas accounting for agricultural systems consider methane and nitrous oxide emissions, carbon emissions from liming and urea use, as well as carbon stock changes, but it ignores gross flows of bio-based energy and carbon. This study compiled data for Canada’s agri-food system over the 2010-13 period, from food supply and disposition to crop processing, animal production, and crop/pasture production. The data were converted to units of energy and carbon, tracked through the agri-food system and compared in scale and conversion efficiency with Canadian crude oil recovery to the production of refined petroleum products. Results showed domestic photosynthesis-derived energy and carbon flow equivalent to 75% and 98%, respectively, of the fossil fuel-derived energy and carbon in the crude oil recovered in Canada. This magnitude is substantial since Canada is a nation with high per capita oil demand that exports over 50% of its own production. Only 14% of the agri-food energy and carbon, respectively, emerged in agri-food products, compared to 91% of the energy and carbon in crude oil that resulted in refined petroleum products. The low conversion efficiency of the agri-food system derived, in part, from 40% of bio-based energy and carbon being diverted to crop and animal residues or waste. Per unit of energy in end products, the other energy inputs (e.g. electricity, fuels) needed to support the agri-food system were 5.3-folds higher in the agri-food system than in the crude oil to products system. This study highlights the need to develop strategies to better utilize the energy and carbon flows of the agri-food system, thereby reducing fossil energy use and greenhouse gas emissions associated with human activities.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".