Call for planning policy and biotechnology solutions for food waste management and valorization in Vietnam
Bibliographic record
Abstract
Food waste (FW) is more harmful than previously imagined. A large amount of Vietnam’s FW ends up in landfills, only 20 % of which are sanitary. This causes significant environmental problems such as greenhouse gas emissions, high carbon footprint, leachate, and landfill-related conflicts. The FW from Vietnam’s urban areas is 0.29 kg⸳p−1⸳d−1, accounting for 31.7 % of total waste. 38.81 % of families discharge FW which, along with municipal waste, corresponds to 4,429.21 ton⸳d−1 for the entire country. For FW collection, under transportation and treatment heads, 80,416.95 $⸳d−1 and 74,605.57 $⸳d−1 were spent, respectively. An analysis of Vietnam’s national strategy for the integrated management of solid waste indicates that the amount of attention and concern currently given to FW issues is not adequate to address them. To resolve FW issues, Vietnam needs to be more proactive regarding solutions and efforts, in addition to implementing strict regulations. These include the setting of national goals under the priority of national strategy, strict regulations, stakeholder engagement, FW recycling to animal feed, biorefinery, and awareness-raising campaigns.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".