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Record W2320197350 · doi:10.1177/154193120204600135

Imaging Systems in Search and Rescue: Implications for Geographic Orientation

2002· article· en· W2320197350 on OpenAlexaff
Jocelyn Keillor, Karen J. Hodges, Michael L. Perlin, Nada Ivanovic, Justin G. Hollands

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2002
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsTechnicianOrientation (vector space)TerrainSearch and rescueComputer scienceFrame (networking)Computer visionArtificial intelligenceProcess (computing)Task (project management)Urban search and rescueGeographyCartographyEngineeringSystems engineeringMathematics

Abstract

fetched live from OpenAlex

Optical imaging systems have the potential to dramatically change the task of a search and rescue technician. An important difference between the traditional process of “looking out the window” and search conducted with the aid of an optical imaging system is that in the case of imaging systems the frame of reference for the viewed display is de-coupled from the technician's frame of reference. We examined the ability of a moving-map display that recorded the locations of designated targets to support geographic orientation in operators with and without knowledge of the modeled terrain. Participants who did not have knowledge of the terrain benefited from the moving map, as when it was present they were less likely to re-identify targets that they had already viewed, whereas those who were already familiar with the terrain model showed no benefit from this manipulation. Both groups had difficulty localizing targets on a map following flight, and the two groups did not differ in their ability to initially detect targets using 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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.294

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 head0.021
GPT teacher head0.242
Teacher spread0.221 · 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

Citations5
Published2002
Admission routes1
Has abstractyes

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