MétaCan
Menu
Back to cohort
Record W2027250845 · doi:10.1121/1.4777941

Ferret and its applications

2005· article· en· W2027250845 on OpenAlexaffabout
Jacques Bédard

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2005
Typearticle
Languageen
FieldEngineering
TopicFire Detection and Safety Systems
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsMuzzleSoftware deploymentSupersonic speedComputer scienceMuzzle velocityAcousticsSoftwareProjectileAerospace engineeringAeronauticsGeologyComputer hardwareTelecommunicationsPhysicsEngineeringOperating systemMechanical engineering

Abstract

fetched live from OpenAlex

Ferret is an acoustic system that detects, recognizes and localizes the source and direction of small arms fire. The system comprises a small array of microphones and pressure sensors connected to a standard PC-104 computer that analyzes, displays, reports and logs the parameters of a recognized shot. The system operates by detecting and recognizing the ballistic shock wave created by the supersonic bullet, combined with the muzzle blast wave propagating from the weapon. The Canadian Land Force Test and Evaluation Unit evaluated a vehicle-mounted version of the system and recommended deployment of the system during peacekeeping missions. The system is the result of a collaborative effort between Defence R&D Canada and MacDonald Dettwiler and Associates. This presentation describes the hardware and software components of the system along with the current and future applications of the system.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.065
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0030.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0650.029

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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicFire Detection and Safety SystemsFrench-language works237,207