The effects of gases from food waste on human health: A systematic review
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
Food waste is a routine and increasingly growing global concern that has drawn significant attention from policymakers, climate change activists and health practitioners. Amid the plurality of discourses on food waste-health linkages, however, the health risks from food waste induced emissions have remained under explored. This lack of evidence is partly because of the lack of complete understanding of the effects of food waste emissions from household food waste on human health either directly through physiological mechanisms or indirectly through environmental exposure effects. Thus, this systematic review contributes to the literature by synthesizing available evidence to highlight gaps and offers a comprehensive baseline inventory of food waste emissions and their associated impacts on human health to support public health decision-making. Four database searches: Web of Science, OVID(Medline), EMBASE, and Scopus, were searched from inception to 3 May 2023. Pairs of reviewers screened 2189 potentially eligible studies that addressed food waste emissions from consumers and how the emissions related to human health. Following PRISMA guidelines, 26 articles were eligible for data extraction for the systematic review. Findings indicate that emissions from food waste, such as hydrogen sulphide, ammonia, and volatile organic carbons, can affect human endocrine, respiratory, nervous, and olfactory systems. The severity of the human health effects depends on the gaseous concentration, but range from mild lung irritation to cancer and death. This study recommends emission capture technologies, food diversion programs, and biogas technologies to reduce food waste emissions.
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) | 0.002 | 0.001 |
| 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