Bibliographic record
Abstract
This annual report of the European Environment Agency (EEA) provides a summary of the information reported on the production, import, export and destruction of fluorinated greenhouse gases (F-gases) in the European Union since 2007 as required by the EU F-gases Regulation 517/2014 (FGR) and previously under Regulation 842/2006: Each year, under the requirements of FGR Article 19, companies that produce, import, export or destroy F-gases, or that use F-gases as feedstock, as well as companies importing products or equipment containing F-gases, must report information relating to the respective amounts of gases or gas mixtures. Furthermore, where applicable, intended uses of gases supplied to the EU market or to types of products or equipment must be reported. F-gases subject to reporting are 19 hydrofluorocarbons (HFCs), 7 perfluorocarbons (PFCs), sulphur hexafluoride (SF6), 5 unsaturated hydro(chloro)fluorocarbons (H(C)FCs), 34 fluorinated ethers (HFEs) and alcohols, and 4 other perfluorinated compounds, including nitrogen trifluoride (NF3), as specified in FGR Annex I and Annex II. The present report presents aggregated trends on production, import, export and destruction of F-gases based on reporting provided by the companies, as well as data assessments related to trends in the supply of F-gases and EU compliance with the HFC phase-down schemes under the FGR and under the Montreal Protocol. Since 2012, the European Commission has given the responsibility for collecting, archiving and evaluating the data reported by companies to the European Environment Agency (EEA). The reporting process is executed through the EEA’s online platform, the Business Data Repository (BDR). Technical support for the F-gas reporting process as well as data assessment is provided by the EEA’s European Topic Centre on Climate Change Mitigation (ETC CM).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.042 |
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".