Dietary modeling of greenhouse gases using OECD meat consumption/retail availability estimates
Bibliographic record
Abstract
Abstract Research has demonstrated different carbon footprints, based on portion estimations. However, previous estimates are low and often omit the impact of food waste. For example, a high-level of daily meat consumption has been estimated at 100 g, which is less than a typical “quarter pounder” hamburger. We used the Organization for Economic Co-operation and Development (OECD) annual estimates of national retail availability, and applied a mathematical model to prorate other research results to determine a meat portion equal to current OECD statistics, and also projected the diets to 2500 and 3250 kcal, to include consumer and retail waste. Once prorated, the 14 national studies are contrasted and analyzed for reasonableness against OECD data pertaining to U.S., U.K., E.U., vegetarian and vegan diets. We quantify how previous studies underestimated greenhouse gas (GHG) emissions and show that previous GHG study results for the highest tier most accurately predict average national dietary consumption.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".