A bibliometric analysis of research on the energy-water nexus from 1963 to 2016 based on SCI-E/SSCI databases
Bibliographic record
Abstract
Using the bibliometric method, this paper characterises literature regarding the energy-water nexus from 1963 to 2016 based on the Web of Science. The results indicate that the USA, the People's Republic of China and Canada were predominant in this field. The Chinese Academy of Sciences was the highest-yield research institute; the Journal of Power Sources was the most productive journal; and cooperation among prolific authors centred mainly on 2 or 3 people. Using co-keyword analysis, the current key research areas in this field are as follows: water management, life cycle assessment, PEM fuel cell, desalination and renewable energy, energy efficiency, and climate change. The most salient finding is that there is significantly more research from the micro-level perspective than the macro-level perspective, which means that to fully understand relationships relevant to water and energy issues, more research is needed.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.273 | 0.379 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".