The GHG Protein Ratio: An Indicator Whose Time Has Come
Bibliographic record
Abstract
The Carbon Footprint (CF) of agriculture must be substantially reduced to help avoid catastrophic climate change. This paper examines the ratio of Greenhouse Gas (GHG) emissions to protein as an indicator of the CF of the major Canadian livestock commodities using previously published results. The GHG emissions for these commodities were estimated with a spreadsheet model that accounted for all three GHGs, the complete life cycles of each livestock type and the livestock interactions with the agricultural land base. The indicator results reviewed here included the responses to livestock types and diets, livestock versus plant protein sources, spatial scales and geographic differences. The sensitivity of the results shown suggest that GHG-protein ratios could provide valuable guidance for producers and consumers to reduce their GHG emissions. For example, diverting feed grains from beef feedlots to hog production would substantially reduce the CF of red meat, although still not as low as the CF of poultry products. The complete proteins derived from pulses have much lower CF values than all livestock products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".