Sustainable Cost-Effective Solution of Climate Emergency With Many More Societal Benefits
Bibliographic record
Abstract
It is very common these days to read news items like summer heat hits Asia early, killing dozens[1]or Dubai airport flooded in hours as storm dumps heaviest rain ever recorded in the desert nation of UAE etc.[2]. These data and many other such reports indicate that climate emergency is already here. According to recent report of Bloomberg New Energy Finance (BNEF)[3], the window to reach net-zero emissions by 2050 is rapidly closing. However, by taking decisive actions it is possible to get on track. Failure to take these decisive actions, even a$1.75~^{\circ }\text{C}$global warming target will be out of reach[3]. Thus, humanity has to use the best possible technology and create new policies to avoid these catastrophic effects of climate emergency. An earlier paper[4]highlighted and explained the important role of power electronics in solving climate emergency. More recently, one of the authors briefly outlined the urgency to electrify everything and accelerate the green energy transition[5]. All current and future power systems must provide high level of resiliency and reliability. In addition, we must consider energy efficiency and cost of power systems as design criterion to achieve the goal of sustainability to mitigate the aftereffects of climate emergency. The purpose of this article is to focus on technical details and discuss policy changes required accelerating the green energy transition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.014 |
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".