Bibliographic record
Abstract
The year is 1970. An engineer with one of the world's most innovative protective relay developers glides an office chair back toward her desk. She sits in a massive, windowless room lit by high-bay fluorescents; the low hum of fans is the only soundtrack to her work. She checks the system specification again, squints toward the wall behind her, and finally gets up to reconfigure the final components. The perimeter of the room is lined with panels of analogue circuit components - thousands upon thousands in total - and she's spent the better part of a week reconfiguring them. With several other groups waiting to use the analogue simulator, she's been working as quickly as possible. Her motivation is no more than a series of satisfying clicks: the tripping of electromechanical distance protection relays, which will give her team the confidence to provide the technology to a customer in a novel network context. To see the relay operate in response to a true, albeit scaled-down, fault signal in the laboratory environment - that assurance is worth the unprecedented investment in the equipment that surrounds her.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".