Bibliographic record
Abstract
The world is facing a climate emergency. We must reduce our reliance on fossil fuels and their export and, instead, develop renewable and efficient energy. Electrification of heating with heat pumps can radically reduce natural gas use, electrical vehicles cut the need for oil, and energy efficiency and renewable energy can help shoulder the greater demand this electrification causes, while cutting coal use, carbon emissions and resultant climate destabilization. Open source hardware design has proven to be an effective method to increase innovation and decrease costs of equipment. It can accelerate practical action and the implementation of policies and strategies to reduce our use of oil and gas. In this presentation the power of open source will be explained to show how it can improve the performance and decrease the costs of decarbonization technologies. This presentation will end with a description of the [HardwareX special issue on Open Source Decarbonization for a Sustainable World](https://www.journals.elsevier.com/hardwarex/forthcoming-special-issues/special-issue-on-open-source-decarbonization-for-a-sustainable-world).
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.095 | 0.060 |
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".