The hidden climate cost: Food loss, waste, and greenhouse gas emissions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The hidden climate cost: Food loss, waste, and greenhouse gas emissions Professor Gordon Price from Dalhousie University and Professor Grant Clark from McGill University study the hidden climate change costs of food loss and waste in Canada. Here, they highlight the need for greater cooperation and data sharing. What connects a meal left uneaten, crops left to rot in the field, and spoiled produce buried in a landfill? They all contribute to the more than one billion tonnes of food that is lost or wasted globally every year (UNEP, 2024). Food loss and waste (FLW) is an important source of greenhouse gas emissions (GHGs) and consequently a driver of climate change. The intertwined relationship between food policy, production, and consumption requires that government, industry, and consumers take immediate and collective action to reduce FLW and its environmental impact.
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.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it