Reliability and Accuracy of Real-Time Visualization Techniques for Measuring School Cafeteria Tray Waste: Validating the Quarter-Waste Method
Bibliographic record
Abstract
Measuring food waste is essential to determine the impact of school interventions on what children eat. There are multiple methods used for measuring food waste, yet it is unclear which method is most appropriate in large-scale interventions with restricted resources. This study examines which of three visual tray waste measurement methods is most reliable, accurate, and cost-effective compared with the gold standard of individually weighing leftovers. School cafeteria researchers used the following three visual methods to capture tray waste in addition to actual food waste weights for 197 lunch trays: the quarter-waste method, the half-waste method, and the photograph method. Inter-rater and inter-method reliability were highest for on-site visual methods (0.90 for the quarter-waste method and 0.83 for the half-waste method) and lowest for the photograph method (0.48). This low reliability is partially due to the inability of photographs to determine whether packaged items (such as milk or yogurt) are empty or full. In sum, the quarter-waste method was the most appropriate for calculating accurate amounts of tray waste, and the photograph method might be appropriate if researchers only wish to detect significant differences in waste or consumption of selected, unpackaged food.
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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 | 0.067 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".