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
Abstract Hybrid and plug‐in hybrid electric vehicles are vehicles that combine the use of both petroleum fuels and electricity as their energy sources to propel vehicles and power accessory systems. In general, they are more fuel efficient and environment friendly than conventional petroleum‐powered vehicles in the same class, while being more financially accessible and technically established than pure electric vehicles; thus, hybrid and plug‐in hybrid electric vehicles serve as significant players in the progress of vehicle electrifying transition and result in remarkable market influence in the automotive industry. This article introduces the fundamental concepts of hybrid electric vehicles and plug‐in hybrid electric vehicles, respectively. It gives an overview of the hybrid technologies, current development, benefits, and advantages that these two hybrids have achieved. Different categories and configurations are specified in each type in detail, while diverse operation modes and control strategies are analyzed and illustrated. In addition, parallel comparisons between hybrid electric vehicles, plug‐in hybrid electric vehicles, conventional vehicles, and electric vehicles are made throughout the article in terms of their similarities and differences. Also, the challenges surrounding the future development of hybrid and plug‐in hybrid electric vehicles are discussed at the end of the article.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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