Bibliographic record
Abstract
Energy is vitally needed to bring electric power to the one quarter of the world’s population that currently lacks it, to sustain economic growth and productivity in many countries. However, there are other problems as climate change which are receiving unprecedented levels of importance in affecting regional, national and global energy policy decisions. Pollution generated by large companies affect to people especially vulnerable persons.This journal will strive to continue delivering high quality and geographically balanced research articles on major topics related to energy and environmental sciences with two issue/year format. Both fundamental and applied aspects are equally represented by invited contributions from rising young scientists as well as more established ones from many different fields. Moreover the new science, knowledge, and applications being discovered and investigated, the public awareness, perception, and understanding of energy and environmental sciences is also of tremendous importance for the implementation and commercial success of such revolutionary technology.With the journal of Energy & Environmental Sciences we intend to pursue such an educational direction and sincerely believe the journal will contribute to a better understanding of an exciting new field of science and get large solutions to the society. Finally, all the authors, guest editors, referees, contributors, and readers are greatly acknowledged for their support and consideration for this journal.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".