Bibliographic record
Abstract
Abstract This book examines the historical origin of the attempts to understand, control, and use noise in modern times. Today, the concept of noise is employed to characterize random fluctuations in general. Before the twentieth century, however, noise only meant disturbing sounds. In the 1900s–50s, noise underwent a conceptual transformation from unwanted sounds that needed to be domesticated into a synonym for errors and deviations in all kinds of signals and information. It is argued that this transformation proceeded in four stages. The rise of sound reproduction technologies—phonograph, telephone, and radio—in the 1900s–20s prompted engineers to tackle unwanted sounds as physical effects of media through quantitative representations and measurements. Around the same time, physicists developed a theory of Brownian motions for random fluctuations and applied it to electronic noise in thermionic tubes of telecommunication systems. These technological and scientific backgrounds led to three distinct theoretical treatments of noise in the 1920s–30s: statistical physicists’ studies of Brownian fluctuations’ temporal evolution, radio engineers’ spectral analysis of atmospheric disturbances, and mathematicians’ measure-theoretic formulation. Finally, during and after World War II, researchers working on the military projects of radar, gunfire control, and secret communications converted the interwar theoretical studies of noise into tools for statistical detection, estimation, prediction, and information transmission. In so doing, they turned noise into an informational concept. Since the grappling with noise involved multiple disciplines, its history sheds light on the interactions between physics, mathematics, mechanical technology, electrical engineering, and information and data sciences in the twentieth century.
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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 | 0.001 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.012 |
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".