Visualization and Analysis of Mapping Knowledge Domains for Food Waste Studies
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 and loss is a global issue involving ethics, society, the environment, and the economy. However, there is a lack of systematic and visual scientific knowledge and graph methods to study the precedents of this field's development over time. The article is based on the scientific knowledge graph research of articles published in the past 22 years to review the latest food waste research developments. The study will be conducted from the following perspectives: country/region, institution, author, journal, keyword co-occurrence, and article co-citation. It turns out that in the past eight years, food waste research has grown rapidly. A total of 8298 research articles have been published in 8064 journals and 176 Web of Science (WOS) subject categories. Research shows in the past 20 years. The main research hotspots were anaerobic digestion, biogas production, composting, biological hydrogen production, and innovation in system management methods. In the future, efficient and multitask biological value-added conversion technology, systematization of food-supply-chain decision-making aid models, and research on differences in management strategies may become the frontiers of research.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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