Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the 11th grade, when most people are thinking about college, Miranda Wang and Jeanny Yao had plastic on their minds. During an environmental-club field trip in 2011, the two visited a waste transfer station in Vancouver, British Columbia, and had a life-changing experience. “We were shocked to see how much plastic was in the garbage” and that it wasn’t being recycled, Wang says. The trip set the women on a life course. Wang and Yao saw a need for some new kind of technology to process plastics, Wang says. So they built one. In 2015, they founded BioCellection, a Menlo Park, California, start-up focused on breaking down polyethylene waste and changing it into a usable commodity. The company is focusing on polyethylene, Wang and Yao say, because recycling technology for the popular plastics poly(ethylene terephthalate) and polystyrene already exists, but there’s a hole in the recycling landscape where polyethylene
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it