MétaCan
Menu
Back to cohort
Record W2117572508 · doi:10.1145/1815396.1815576

Continuing progress in augmenting urban search and rescue dogs

2010· article· en· W2117572508 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
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSearch and rescueComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Canine Augmentation Technology (CAT) is a telepresence system worn by search canines to be used in Urban Search and Rescue (US&R) operations. The intended purpose of CAT is as a tool for search teams and emergency managers to sense the situation when the dog finds a survivor in a collapsed structure. Data about the environment is transmitted to searchers and managers from the dog who may be able to penetrate further into a rubble pile than humans. Certain critical information can help the rescue team by allowing them to understand the situation around the victim before they actually attempt the rescue. This paper describes the latest developments in the CAT prototypes as well as discusses the improvements from previous versions and makes comparisons to other telepresence systems used in US&R operations.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.253

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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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

Citations16
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicModular Robots and Swarm IntelligenceFrench-language works237,207