Bibliographic record
Abstract
In the hopes of slowing climate change, researchers are seeking ways to get rid of planet-warming carbon dioxide. Making something valuable from it in the process, such as commodity chemicals, is a double win. Researchers at the University of Toronto and the California Institute of Technology now report they have carried off such a trick by improving the efficiency of a process to make the plastics precursor ethylene from CO2 electrochemically (Nature 2019, DOI: 10.1038/s41586-019-1782-2). There’s a huge existing market for ethylene, says Edward Sargent, an electrical engineer at the University of Toronto. But the compound also has a big CO2 footprint because its manufacture is fossil fuel based. “So if we could instead make renewable ethylene,” Sargent says, “we could displace some of the use of fossil fuel–derived ethylene, and we could consume rather than emit CO2 while we’re doing it.” For this work, Sargent and coworkers teamed up
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 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) | 0.002 | 0.000 |
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 teacher head, 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".