5. Impact of the Digital Revolution on Worldwide Energy Consumption
Bibliographic record
Abstract
We Tweet, Facebook, Netflix and YouTube in the palm of our hand. We are aware of the amount of energy that it takes from how many times that we need to recharge our devices. However, this is just the tip of the iceberg. For every joule of energy we expend locally, many more joules are spent in the backbone of the Internet. While our appetite for data has largely been insatiable over the last thirty years, the energy required to sustain this has been held in check by Moore’s Law’s driving creed that density of function in a computer chip increases by two every two years, and energy/function decreases by a similar amount. With that said, this driving relationship between power consumption and computing density is slowing due to a multitude of physical constraints when the density of transistor packing approaches the limits. In the following chapter, the authors examine these relationships and outline some of the challenges that the world is facing as we continue to meet and exceed the expectations of our data-driven world with a finite growth in worldwide power generation capacity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.010 |
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".