Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Materials have the potential to be the centrepiece for the transition to viable renewable energy technologies if they realise a specific suite of properties and achieve a desired set of performance metrics. The envisioned transition involves the discovery of materials that enable generation, conversion, storage, transmission, and utilization of renewable energy. This book presents, through the eye of materials chemistry, an umbrella view of the myriad of classes of materials that make renewable energy technologies work. They are poised to facilitate the transition of non-renewable and unsustainable energy systems of the past into renewable and sustainable energy systems of the future. It is a story that often begins in chemistry laboratories with the discovery of new energy materials. Yet, to displace materials in existing energy technologies with new ones, depends not only on the ability to design and engineer a superior set of performance metrics for the material and the technology but also the requirement to meet a demanding collection of economic, regulatory, social, policy, environmental and sustainability criteria. Disruption in the traditional way of discovering materials is coming with the emergence of artificial intelligence, machine learning and robotic automation designed to accelerate the well-established discovery process, massive libraries of materials can be evaluated and the possibilities are endless. This book provides a perspective on the application of these new technologies to this field as well as an overview of energy materials discovery in the broader techno-economic and social context. Any budding researcher or more experienced materials scientist will find a guide to a fascinating story of discovery and emerge with a vision of what is next.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.320 | 0.002 |
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 it