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Record W2083232350 · doi:10.1177/1541931213571041

Blue Force Tracking

2013· article· en· W2083232350 on OpenAlexaff
Geoffrey Ho, Justin G. Hollands, Michael Tombu, Ken Ueno, Matt Lamb

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2013
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsGlobal Positioning SystemAdversaryWorkloadComputer scienceTracking (education)Scope (computer science)Computer securityHuman–computer interactionSimulationTelecommunicationsPsychologyOperating system

Abstract

fetched live from OpenAlex

Blue force tracking (BFT) is a military technology that provides positional awareness of friendly forces on a digital map through global positioning system (GPS) technology. For dismounted soldiers, having readily available information on the location of friendly forces can be critical for mission success. However, GPS can report positions that are spatially inaccurate. The present study required 36 military participants to lead a team through a simulated mission in a virtual environment. The mission required the participant to find and support friendly forces engaged in a firefight with enemy forces. Participants had a digital map, an unreliable BFT device, or a perfectly reliable BFT device. The results indicated that participants using BFT engaged enemy forces more quickly, used their BFT to gain a wider scope of their environment, and had lower workload. For most measures, there were no significant differences between reliable and unreliable BFT, suggesting that even an unreliable BFT can provide benefits to soldier performance.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.524

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 studies0.0010.000
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 head0.023
GPT teacher head0.287
Teacher spread0.264 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

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