MétaCan
Menu
Back to cohort
Record W4253748892 · doi:10.1121/2.0000564

Fessenden and Boyle: Two Canadian sonar pioneers

2017· article· en· W4253748892 on OpenAlexaffabout
Harold M. Merklinger, Dale D. Ellis

Bibliographic record

VenueProceedings of meetings on acoustics · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDalhousie UniversityDefence Research and Development Canada
Fundersnot available
KeywordsNavySubmarinePower (physics)EngineeringSonarTelecommunicationsHistoryAeronauticsLibrary scienceLawArt historyElectrical engineeringOceanographyPolitical scienceComputer scienceGeologyMarine engineeringPhysics

Abstract

fetched live from OpenAlex

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 Board of Invention 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.261
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of meetings on acousticsSame topicUnderwater Acoustics ResearchFrench-language works237,207