Do date codes cause food waste? Smart packaging might tackle the problem
Bibliographic record
Abstract
Food waste is a multifaceted problem that occurs across various sectors of the food supply chain, causing significant repercussions on the environment, food security, and both global and regional/national economies. One of the most common reasons for food waste is the misunderstanding of food dating (e.g., best-before date), leading to the throwing away of food more frequently. Consumers tend to discard food products as they approach the best-before date due to potential health and safety concerns. Many argue that the diversity of date labels employed by food manufacturers contributes to confusion among consumers regarding both food quality and safety, thus causing food waste. Removing/ supplementing date codes requires the adoption of alternative methods to maintain the freshness and safety of food. Recent advancements in smart packaging have the potential to extend the shelf life and maintain the safety of products equipped with a range of features, which allow the monitoring of the condition of packaged products and provide information. Given the global concern over food waste, this review emphasizes the implementation of smart (active and intelligent) packaging in order to supplement the date labels and assist consumers in reducing their waste and maintain the integrity of its bioactive components.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".