SETIS Magazine: Energy Systems Modelling
Bibliographic record
Abstract
The SETIS magazine aims at delivering timely information and analysis on the state of play of energy technologies, related research and innovation efforts in support of the implementation of the European Strategic Energy Technology Plan (SET-Plan). The current issue is dedicated to Energy Systems Modelling\n\nThe editorial for the Energy Systems Modelling issue is provided by Dr Jan Nill at the European Commission’s Directorate-General for Climate Action. This issue also hosts interviews with:\n•\tDavid Connolly – coordinator of the H2020 project "Head Roadmap Europe" and one of the developers of the EnergyPLAN model;\n•\tMarc Oliver Bettzüge – director of the Institute of Energy Economics at the University of Cologne (ewi); Alistair Buckley - co-author of ‘A review of energy systems models in the UK: Prevalent usage and categorisation’; and\n•\tMark O'Malley – director of the International Institute for Energy Systems Integration.\n\nThree contributors from the European Commission’s Directorates-General for Climate Action, Energy and Mobility and Transport collaborate on an article on the EU Reference Scenario 2016, one of the European Commission’s key analysis tools used in the context of the Energy Union. We also have a contribution from DG Energy on the METIS energy system model - a research project for the development of energy simulator software with the aim to further support DG ENER’s evidence-based policy-making. Finally, the Joint Research Centre Directorate C - Energy, Transport and Climate contributed to an article on the importance of open data and software for energy research and policy advice.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.087 | 0.033 |
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".