Bibliographic record
Abstract
In recent years, new-found interest in the hydrogen economy from both industry and academia has helped to shed light on its potential. Hydrogen can enable an energy revolution by providing much needed flexibility in renewable energy systems. As a clean energy carrier, hydrogen offers a range of benefits for simultaneously decarbonizing the transport, residential, commercial and industrial sectors. Hydrogen is shown here to have synergies with other low-carbon alternatives, and can enable a more cost-effective transition to de-carbonized and cleaner energy systems. This paper presents the opportunities for the use of hydrogen in key sectors of the economy and identifies the benefits and challenges within the hydrogen supply chain for power-to-gas, power-to-power and gas-to-gas supply pathways. While industry players have already started the market introduction of hydrogen fuel cell systems, including fuel cell electric vehicles and micro-combined heat and power devices, the use of hydrogen at grid scale requires the challenges of clean hydrogen production, bulk storage and distribution to be resolved. Ultimately, greater government support, in partnership with industry and academia, is still needed to realize hydrogen's potential across all economic sectors.This article is part of the themed issue 'The challenges of hydrogen and metals'.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".