Reducing Food Wastage Through Accurate Demand Prediction Using Generative AI
Bibliographic record
Abstract
Mitigation of food wastage is an essential part of solving global problems that have a harsh impact on the economy, ecology, and population. According to FAO, around one third of all food produced around the world, or about 1.3 billion tones per year, is wasted. Not only does this inefficiency have a safeguard of cost the global economy over $1 trillion each year, but environmental degradation is worsened as well. The breakdown of organic waste by bacteria in landfill sites accounts for between 8% and 10% of global greenhouse gas emissions, with methane, which worsens climate change. Across the retail stores as well as the hospitality business, food waste is most common mainly due to complications in estimating the market demand for the products accurately. Seasonal changes, events that can not be foreseen and changes in consumers demand make the accuracy of forecasting rather low, often leading to the overproduction and products' spoilage.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".