Second Life Plastic Project: Using a One Health Lens to Address Plastic Bottle Pollution
Bibliographic record
Abstract
One Health is an intersectional approach to balance and optimize the health of humans, non-human animals, and the environment. Single-use plastics have become a major part of modernity, but their improper disposal has devastating impacts on the One Health triad. Current strategies to address plastic pollution include the ban on single-use plastic items and modification of the trade flow of waste aids. While these strategies are effective, they are not sustainable enough to produce the long-term, large-scale impacts that are needed. This paper proposes the implementation of a smallscale One Health initiative aimed at reducing plastic pollution. Specifically, plastic bottles will be collected, then undergo shredding and repurposing by a 3D printer to create furniture pieces such as chairs and storage containers that will be sold to customers. Repurposing plastic bottles provides a sustainable long-term solution that reduces the amount of plastic waste ending up in landfills, protecting the health of humans, non-human animals, and the environment. If proven successful, this local initiative could be leveraged by additional communities to grow into a worldwide application.
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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".