Reliability and Accuracy of Real-Time Visualization Techniques for Measuring School Cafeteria Tray Waste: Validating the Quarter-Waste Method
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
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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