Design and Development of a Portable and Cost-effective Shredder for On-site Volume Reduction of Plastic and Paper Waste
Bibliographic record
Abstract
We present a compact, portable, and cost-effective plastic shredder suitable for use in various community and private places including offices, cafes and homes. Our prototype is capable of reducing the volume of both plastic and paper based disposable drinkware and containers by up to 70%, which may result in lower carrying costs and potential reductions in carbon emissions from transportation since the waste would be possible to be shredded directly on-site after it is discarded. Unlike a traditional shredder, the proposed prototype eliminates the need for additional accessories such as gears, pulleys, or chains, as the shaft is directly powered by an electric motor. Furthermore, the shredder is equipped with an automatic detection system that triggers the shredding process upon detecting plastic objects and stops the operation automatically when the shredding is completed, resulting in reduced energy consumption. Rectangular chips of shredded plastic/paper with an average size of 5×10 to 10×10 mm are produced, which demonstrates the potential of reducing plastic waste on-site in a cost-effective and sustainable manner.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".