Bibliographic record
Abstract
Although made of paper, most coffee cups are not recycled because of the polyethene covering their internal surface area (1, 2). Instead, they are sent to landfills where they break down into microplastics and negatively impact organisms after entering the food chain (2, 3). This is an especially alarming issue due to the extensive usage of paper coffee cups around the world. As a result, many global companies have been searching for an eco-friendly cup that eliminates the use of polyethene, a challenge that remains unresolved to this day (9). While the search continues, many businesses have relied on temporary strategies to reduce polyethene production until a design that eliminates its use is developed (9, 10, 11, 12). Two major methods include public awareness and promotion of reusable cups (2). However, these approaches have only resulted in minor changes due to their reliance on customer cooperation (2). To guarantee polyethene reduction, this report proposes a strategy that is independent of customer cooperation. This method determines the dimensions (i.e., height and bottom radius) that minimize the amount of polyethene needed to coat the internal surface area of a cup, while keeping the cup’s volume and lid size (i.e., top radius) the same. The resulting equation gives the surface area of the cup while the root to the first derivate of this equation corresponds to the optimal bottom radius. Using a derived equation for height, the optimal cup height is determined as well. To highlight its proper implementation, this strategy is applied to a Starbucks Grande coffee cup as a model for other companies to follow.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".