Bibliographic record
Abstract
Wastewater from human activity could fill more than 100 million Olympic-size swimming pools in a single year, according to researchers from Canada and the Netherlands ( Earth Syst. Sci. Data 2021, DOI: 10.5194/essd-13-237-2021 ). The waste people send down the drain is rich in energy, but turning that waste into renewable fuels in the most efficient way is critical to making the endeavor economically viable. Scientists have now shown that they can boost both hydrogen and biodiesel production from waste by adding a biodegradable surfactant ( ACS ES&T Engg. 2023, DOI: 10.1021/acsestengg.2c00372 ). Researchers at the Harbin Institute of Technology and Northeast Agricultural University took waste activated sludge—made from raw sewage that had been treated with oxygen to stabilize its microbe populations—from a treatment plant in Harbin, China. They sieved the mixture to remove large particles, heated the sludge, and allowed bacteria to produce hydrogen through fermentation. After that, the
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".