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Record W2126815750 · doi:10.1145/1815396.1815577

Bark indication detection and release algorithm for the automatic delivery of packages by dogs

2010· article· en· W2126815750 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBark (sound)Computer scienceAlgorithmPhysics

Abstract

fetched live from OpenAlex

The Canine Remote Deployment System (CRDS) is a set of equipment for the delivery of emergency supplies to trapped victims in urban search and rescue (US&R) operations. The system utilizes search canines carrying a bag of supplies and equipped with a wireless receiver device. A hand-held wireless transmitter is used by a human to trigger the release of the bag. The canine handler would activate the transmitter upon hearing the dog's "bark indication" that it has found a live victim. In such manner, a package can be deployed from the dog at the press of a button. This paper describes a new feature of the CRDS whereby the release mechanism that deploys the package---called the "underdog" - is activated through the detection of the dog's barks and an algorithmically controlled deployment strategy. A series of experiments were conducted to ensure consistency and accuracy of the bark release algorithm. Results are presented in this paper.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.226
Teacher spread0.219 · 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

Quick stats

Citations13
Published2010
Admission routes1
Has abstractyes

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