Bibliographic record
Abstract
Waste from food and its packaging is increasing worldwide at an unmanageable rate. In the United States, packaging from food makes up nearly a quarter of landfill waste. Additionally, in the U.S. every year over 60 million metric tons of food a year is wasted and 32 million metric tons of it end up dumped in landfills. Almost half of the food purchased by people ends up in the trash because it goes bad too fast. That equates to $162 billion of food that goes to the landfill and adds to the methane issue in our atmosphere. Bare zero waste grocery store helps solve the issues of food and packaging waste. Goods and groceries will be stored in reusable bins and dispensers such as glass containers then to be poured into personal containers brought from home, or ones bought from the store. No item will be wrapped in plastic, and discounts will be offered for bringing your personal reusable containers and bags. Aiding to eliminate food waste, the amount of food purchased by Bare will be decreased and any perishables will be donated to soup kitchens and those in need. To further help the environment, Bare will provide the proper places to recycle atypical products such as batteries, plastic bags, and glass. Presentation Time: Wednesday, 11 a.m.-12 p.m.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.646 | 0.314 |
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".