Editorial: COVID-19 pandemic, food behaviour and consumption patterns
Bibliographic record
Abstract
With already 600 million confirmed cases of COVID-19 and over 6 million recorded deaths, the Coronavirus Disease 2019 (COVID-19), detected in Wuhan (China) in late 2019, is nowadays one of the most pressing global challenges facing humanity. In addition to significantly impacting health systems, the COVID-19 pandemic disrupted food systems from farm to fork, with consequences for food and nutrition security at all levels (global, national, local, and individual). While a growing corpus of research examines the pandemic's disruption of food supply networks, the implications regarding food environments and consumer behavior are still widely overlooked, particularly in developing countries. Accordingly, this Research Topic intends to offer insight into the pandemic's influence on food buying behavior, nutrition, and eating habits and the consequences of these changes. It includes 10 papers on various issues (diet, food security, food affordability, food safety, shopping habits, food waste, etc.) and geographical areas (Oman, Jordan, Saudi Arabia, Italy, Canada, and India).
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.006 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.019 | 0.016 |
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".