Notice of Violation of IEEE Publication Principles: Bitcoin for smart trading in smart grid
Bibliographic record
Abstract
Notice of Violation of IEEE Publication Principles<br><br>"Bitcoin for Smart Trading in Smart Grid"<br> by M.T. Alam, H. Li, and A. Patidar<br> in the Proceedings of the 21st IEEE International Workshop on Local and Metropolitan Area Networks, April 2015<br><br> After 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.<br><br> 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.<br><br> "Privacy-friendly Tasking and Trading of Energy in Smart Grids"<br> by Tassos Dimitriou and Ghassan Karame<br> in the Proceedings of the 28th Annual ACM Symposium on Applied Computing, March 2013<br><br> "NRGcoin: Virtual Currency for Trading of Renewable Energy in Smart Grids,"<br> by M. Mihaylov, S. Jurado, N. Avellana, K. Van Moffaert, I. M. de Abril and A. Nowe<br> in the Proceedings of the 11th International Conference on the European Energy Market, May 2014<br><br> <br/> Privacy 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".