Fluorinated greenhouse gases 2018: data reported by companies on the production, import and export of fluorinated greenhouse gases in the European Union, 2007-2017
Bibliographic record
Abstract
The 2018 edition of the European Environment Agency (EEA) report on fluorinated greenhouse gases (F-gases) confirms the good progress achieved in 2017 by the European Union (EU) in phasing down the use of hydrofluorocarbons (HFCs), a set of fluorinated gases with a high global warming potential (GWP) that is significantly contributing to climate change. The report evaluates and presents the data reported by companies in 2018 about their activities involving F-gases in 2017, assessing both the progress made under the ongoing EU-wide HFC phase-down and the outlook towards the global HFC phase-down, which is due to begin in 2019 under the Kigali Amendment to the Montreal Protocol. The report also details the amounts of F-gases supplied to different industrial applications. The report uses two different metrics: F-gas amounts expressed in physical tonnes reflect the use patterns of F-gases in European industries, while their GWP (in tonnes of carbon dioxide (CO2) equivalents (tCO2e)) are relevant for climate change policy.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".