A Bibliometric Analysis of the “NetZero” Process in the Energy Knowledge Domain
Bibliographic record
Abstract
This work aimed to elucidate the key research findings within the realm of NetZero, specifically within the energy field.Employing advanced data visualization tools, particularly VOSviewer, scientific maps were meticulously crafted to explain the evolving landscape of research in this domain.The results showed that the nations that most vigorously committed to the NetZero endeavor are the United Kingdom, United States, China, Australia, and Canada, signaling a global consensus on the urgency of addressing climate change.Furthermore, this study reveals pivotal trends in the field and keywords such as "renewable energy", "decarbonization", "netzero", and "sustainability" have gained remarkable prominence, especially in recent research.In conclusion, this work offers a comprehensive overview of the NetZero landscape within the energy field, emphasizing the urgency of international collaboration, and identifies key trends that will likely shape the future of sustainable energy research and policymaking.
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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 | 0.009 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.119 | 0.168 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".