Carbon Footprint of Beef Cattle
Bibliographic record
Abstract
The carbon footprint of beef cattle is presented for Canada, The United States, The European Union, Australia and Brazil. The values ranged between 8 and 22 kg CO2e per kg of live weight (LW) depending on the type of farming system, the location, the year, the type of management practices, the allocation, as well as the boundaries of the study. Substantial reductions have been observed for most of these countries in the last thirty years. For instance, in Canada the mean carbon footprint of beef cattle at the exit gate of the farm decreased from 18.2 kg CO2e per kg LW in 1981 to 9.5 kg CO2e per kg LW in 2006 mainly because of improved genetics, better diets, and more sustainable land management practices. Cattle production results in products other than meat, such as hides, offal and products for rendering plants; hence the environmental burden must be distributed between these useful products. In order to do this, the cattle carbon footprint needs to be reported in kg of CO2e per kg of product. For example, in Canada in 2006, on a mass basis, the carbon footprint of cattle by-products at the exit gate of the slaughterhouse was 12.9 kg CO2e per kg of product. Based on an economic allocation, the carbon footprints of meat (primal cuts), hide, offal and fat, bones and other products for rendering were 19.6, 12.3, 7 and 2 kg CO2e per kg of product, respectively.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".