Proceedings of the 2nd ACM SIGCOMM workshop on Green networking
Bibliographic record
Abstract
Welcome to the Second ACM SIGCOMM Workshop on Networking! The energy consumption and environmental impact of networking and communications equipment is of increasing importance to researchers, commercial entities and society at large. The Second ACM SIGCOMM Workshop on Networking aims to continue the exciting first edition of the workshop, to create a dynamic forum for discussing green networking issues. This year we have expanded the scope of the workshop to also present promising research ideas at the nexus of energy and IT domains and in the field of smarter energy systems. This year's call for papers attracted 19 submissions on a diverse set of topics ranging from energy harvesting to data center cooling to characterization of smarter homes to photonic coding. The 14 member Technical Program Committee along with a selected group of external experts carefully considered all of the submissions. The committee had assembled a program composed of 8 papers that will be presented during the workshop. The workshop will also feature a keynote talk on Smart Grids and a panel Green Networking: Tip of the Iceberg or The Polar Bear? featuring leading academia and industry participants.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.093 | 0.059 |
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".