Bibliographic record
Abstract
US president Joe Biden’s recent executive order on climate change includes provisions impacting a class of commercial chemicals and, likely, chemical plants. Biden called for the US to officially join a key environmental treaty controlling a class of synthetic greenhouse gases. The pact ramps down the production and use of hydrofluorocarbons (HFCs), which are used as refrigerants, solvents, and etching agents in silicon chip manufacturing. HFCs replaced two types of chemicals that erode stratospheric ozone—chlorofluorocarbons and hydrochlorofluorocarbons. While HFCs don’t harm the ozone layer, they are potent greenhouse gases. The HFC treaty, reached in 2016 in Rwanda’s capital city, is the Kigali Amendment to the 1989 Montreal Protocol on Substances That Deplete the Ozone Layer. The US signed the Kigali deal but is not yet an official treaty partner. For that to happen, the Senate must give its advice and consent. Biden directed Secretary of State Antony Blinken to formally
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.083 | 0.031 |
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".