Bibliographic record
Abstract
Asphaltenes, a carbon-rich by-product of crude oil production, can be used to make graphene, according to a new study ( Sci. Adv. 2022, DOI: 10.1126/sciadv.add3555 ). Asphaltenes are used for roofing and paving, and some refineries make synthetic fuel from the heavy residue. But unused asphaltenes can be a massive environmental headache. They emit carbon when burned, and if they’re discarded into landfills and tailing ponds—storage facilities for by-products of oil sands mining—they can pollute land, water, and air. Md Golam Kibria of the University of Calgary and colleagues zeroed in on a way to create a higher-value material from asphaltenes using a technique called flash joule heating . Passing a short pulse of current through powdered asphaltenes turns them into a carbon allotrope called asphaltene-derived flash graphene (AFG) in a matter of seconds. No furnaces, solvents, or reactive gases are needed, making the process relatively inexpensive. Because asphaltenes are
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".