Investigating a replacement for disposable chopsticks
Bibliographic record
Abstract
Forests are overexploited for many purposes (food, medicine, shelter, and commercial use). Every year, 25 million trees a year are cut down in China or around 100 acres every 24 hours. Deforestation has led to environmental crises such as soil erosion, flooding, landslides, foot shortages, carbon dioxide abundance, and the extinction of species. One of the main contributors to this deforestation is the mass production of disposable wooden chopsticks. An astonishing 57 billion pairs of these chopsticks are produced annually in China alone, the equivalent of about 3.8 million trees. People are using these chopsticks because they are the least expensive option, and they are not recycling them because it costs more to do so than it does to throw them away. UBC has committed to their vision of sustainability. As a consequence of the new Student Union Building being developed at UBC, there has arisen an opportunity to install a vending machine filled with sustainable products to help UBC students strive towards this vision. Reusable chopsticks are such a product, and it is with this in mind that a triple-bottom line analysis was conducted to determine the expectation and viability of such an endeavor. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".