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Record W2074357131 · doi:10.1109/ssrr.2011.6106781

Initial experiments on 3D modeling of complex disaster environments using unmanned aerial vehicles

2011· article· en· W2074357131 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
TopicRobotics and Sensor-Based Localization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDroneTask (project management)Computer scienceRubbleUrban search and rescueSearch and rescueEmergency managementAeronauticsSimulationReal-time computingHuman–computer interactionSystems engineeringArtificial intelligenceEngineeringCivil engineeringMobile robotRobot

Abstract

fetched live from OpenAlex

The use of unmanned aerial vehicles (UAVs) has the potential to significantly improve the situation awareness of emergency first responders working at urban disaster sites. Having the characteristics of being small, light-weight and quickly deployable, UAVs offer the ability to fly over an urban disaster and provide intelligence to Urban Search and Rescue (USAR) task force efforts before precious operational resources are committed on the ground. In this paper we discuss our experience with using a small UAV to perform the task of creating a 3D model of a rubble pile's surface using commercial off the shelf (COTS) components in the form of an available UAV equipped with a modified video game sensing package.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.418

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 head0.125
GPT teacher head0.268
Teacher spread0.143 · 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

Citations33
Published2011
Admission routes1
Has abstractyes

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