A Video Repository for Innovative Methods of Dietary Assessment and Analysis
Bibliographic record
Abstract
ARTICLES DISCUSSED: Lasschuijt, M. P. et al. Concept development and use of an automated food intake and eating behavior assessment method. Journal of Visualized Experiments. (168), e62144 (2021). Chung, J. et al. Design and evaluation of smart glasses for food intake and physical activity classification. Journal of Visualized Experiments. (132), e56633 (2018). Lucassen, D. A., Brouwer-Brolsma, E. M., van de Wiel, A. M., Siebelink, E., Feskens, E. J. Iterative development of an innovative smartphone-based dietary assessment tool: TRAQQ. Journal of Visualized Experiments. (169), e62032 (2021). Boronat, A. et al. Mobile device-assisted dietary ecological momentary assessments for the evaluation of the adherence to the Mediterranean diet in a continuous manner. Journal of Visualized Experiments. (175), e62161 (2021). Meroni, A., Jualim, N., Fuller, N. 'Boden Food Plate': novel interactive web-based method for the assessment of dietary intake. Journal of Visualized Experiments. (139), e57923 (2018). Mezgec, S., Seljak, B. K. Deep neural networks for image-based dietary assessment. Journal of Visualized Experiments. (169), e61906 (2021). Bromage, S. Integrated spreadsheets for nutritional analysis of population diet surveys. Journal of Visualized Experiments. (188), e64327 (2022).
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.385 | 0.136 |
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".