Notice of Violation of IEEE Publication Principles: Bitcoin for smart trading in smart grid
Bibliographic record
Abstract
Notice of Violation of IEEE Publication Principles"Bitcoin for Smart Trading in Smart Grid"by M.T. Alam, H. Li, and A. Patidarin the Proceedings of the 21st IEEE International Workshop on Local and Metropolitan Area Networks, April 2015After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.This paper duplicates original text from the papers cited below. The original text was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission."Privacy-friendly Tasking and Trading of Energy in Smart Grids"by Tassos Dimitriou and Ghassan Karamein the Proceedings of the 28th Annual ACM Symposium on Applied Computing, March 2013"NRGcoin: Virtual Currency for Trading of Renewable Energy in Smart Grids,"by M. Mihaylov, S. Jurado, N. Avellana, K. Van Moffaert, I. M. de Abril and A. Nowein the Proceedings of the 11th International Conference on the European Energy Market, May 2014Privacy aware anonymous trading for smart grid using digital currency has received very low attention so far. In this paper, we analyze the possibility of Bitcoin serving as the user friendly and effective privacy aware trading currency to facilitate energy exchange for smart grid.
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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 | 0.040 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.031 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.037 | 0.036 |
| Insufficient payload (model declined to judge) | 0.043 | 0.049 |
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".