A View on Surplus Food Donation App
Bibliographic record
Abstract
Abstract: The enormous development in the amount of food waste has prompted a need for charity donations. Food is significantly wasted every day at numerous institutions, including restaurants, parties, social gatherings, university canteens, and many of the other social events in the existing circumstances. Currently, individuals contribute food manually by visiting each agency numerous times to alleviate the concerns with food waste. Although some mechanisms currently in place have made an attempt to aid with food donations, the new web application that is part of the proposed framework offers a platform for recycling extra food to help people who are in need on an individual and group level. This technique has shown to be an effective manner of giving things online to charities, solving the significant problem of food waste. The article includes insights into the aim behind such an application, highlighting the existing process of contributions and how the product operates to serve the community. Under this framework, hotels, restaurants, charities, and individuals would all have access to a single platform for communication. Charities and individuals could then connect with restaurants that have excess food available for immediate donation, and the framework would track the quantity of food donated by each restaurant, awarding food donors with points. The fundamental modules in this design are "Food Donor," which may be any company, organization, or institution eager to provide food and submit new food donation requests, and "Food Receiver," representing meal-seeking charity groups. A new food donation request may be produced on the website, and a message will be issued to the third-party agency responsible for conveying food from the donor to the receiver once the request is allowed.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.110 | 0.046 |
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".