Bibliographic record
Abstract
Reginald Fessenden developed voice-modulated radio in the early 20th century using special alternators and microphones capable of handling kilowatts of power. When the Titanic struck the iceberg in 1912, Fessenden, then in Boston, developed an audio-frequency acoustic transmitter-receiver, which he used to detect an iceberg at two miles. When the “Great War” began, UK interests turned to detecting submarines. Ernest Rutherford, on behalf of the Bureau of Investigation and Research (BIR), invited former students A. B. Wood, and R. W. Boyle, to join the anti-submarine effort. Fessenden was also consulted. The team detected submarines using Fessenden’s device but experienced difficulty determining target bearing. After consulting Paul Langevin, Boyle agreed that quartz transducers operating at ultrasonic frequencies should provide a solution. Boyle then developed the UK Type 112 “ASDIC” which was being fitted to Royal Navy warships just as the War ended. Boyle returned to the University of Alberta after the war and continued work in ultrasonics. In 1929 he was appointed Director of Physics at Canada’s National Research Council. There he established an Acoustics Section, and during World War II started a scientific activity in Halifax, NS, which later became the Naval Research Establishment.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".