Bibliographic record
Abstract
Baker Hughes has invested in Ekona Power, a Canadian start-up developing a low-carbon process for making hydrogen via methane pyrolysis. Ekona uses combustion and high-speed gas dynamics to turn methane into hydrogen and solid carbon. Ascribe Biosciences , a crop protection company started in a Cornell University lab, has raised $2.5 million in seed funding to commercialize a broad-spectrum biopesticide. The funding will expand field trials for wheat, soy, potatoes, and other crops. Givaudan , a Swiss flavor and fragrance company, is acquiring the US-based fragrance creator Custom Essence, which specializes in natural fragrances. Custom Essence has annual sales of about $40 million. Codexis , an enzyme engineering firm, has invested $10 million in Molecular Assemblies, which is developing an enzymatic process for DNA synthesis. Codexis is helping to improve the DNA polymerase enzymes that the process uses. Jubilant Biosys will provide discovery chemistry to the San Diego–based cancer drug
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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.780 | 0.775 |
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".