Bibliographic record
Abstract
All food production has a significant environmental impact, and regardless of whether the produced food is eaten or not, it affects several of Sweden’s environmental quality objectives. Information from 2003 shows that the food consumption in Sweden is responsible for about 20 million tons of carbon dioxide equivalents, or approximately 2 tons of carbon dioxide per person and year. The avoidable food waste is a part of the total food waste and most of it comes from households, grocery stores, restaurants, food industry and school kitchens. The avoidable food waste from restaurants alone is considered about 62 % of the restaurants total food waste, wherefore there is a chance of reducing the restaurants’ food waste. This study has shown that restaurant owners are open to political solutions and policy instruments such as information and education to reduce food waste, but not law-enforced instruments. However, the interviewed owners are doubtful of a standardized system of portion sizes because they believe it to be difficult to apply and implement in restaurants. This study has also shown that restaurant patrons generally feel that the portion size is a little more than what they can eat, and therefore they also leave less than a quarter of their portion. The amount of food left behind is about 100–150g, or 5–10 % of the entire portion according to the interviewed restaurant owners. The study has also revealed tendencies that the restaurants patrons leave behind rice, pasta, and root vegetables from their serving. From this investigation it can be concluded that there is a possibility to reduce portion sizes in order to achieve the environmental quality objectives that Naturvårdsverket suggested in order to reduce the food waste.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.600 | 0.515 |
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".