The Largest and Most Complex (Man-Made) Machine
Bibliographic record
Abstract
in the World • Transmission from X to Y can be 1,000s of miles. • All generators and motors spin at about the same speed at the same time. • A problem in Florida felt in Manitoba. PIX 10926 A Market Like No Other • So other than that what makes an energy market so unique? A Market Like No Other • Electrical energy cannot be stored: • It can be converted to other forms of energy and stored but for very large costs and efficiency losses (e.g., pumped hydro plants). • Energy is generated and consumed at almost the exact same time: • Once the corn is harvested, it must be sold, transported, and eaten in a fraction of a second. • Energy must be transported to consumers at the speed of light often from far distances. • Laws of physics will dictate where power will go, who will get it, and how much of it will be lost along the way; NOT laws of economics: • If the road is full of trucks, you can’t deliver anymore supply, and you can’t use a different road. • There are many different ways to supply it, but the end product is the exact same thing no matter how it is supplied: • Some suppliers have large capital costs and low variable costs, others are the opposite (price highly volatile even throughout day). Outline
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.079 | 0.040 |
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".