Food and beverages industry group launches new recycling initiative
Bibliographic record
Abstract
A new initiative from the Carton Council—a coalition of leading food and beverage carton manufacturers Elopak, Pactiv Evergreen, SIG and Tetra Pak—will enable schools across the United States and Canada to boost their recycling efforts with financial support. Grants of up to $5,000 will be available to help U.S. and Canadian K–12 schools establish, enhance or expand food and beverage carton recycling programs. The initiative aims to address an issue that has a lot of room for improvement. The average school consumes an estimated 75,000 milk, juice and other food and beverage cartons, but only 10% of U.S. primary and secondary schools currently recycle them, the Council said. Grant funds can be used to purchase sorting equipment or collection bins, create communications, print signage, establish a “green team” or other activities that support school carton recycling. Past recipients have used similar grants to launch new recycling programs or improve existing ones, reduce waste and engage students in educational sustainability efforts, the Council said.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.046 | 0.016 |
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".