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Record W1912629087 · doi:10.1109/ccece.2005.1557130

Handheld landmine avoidance system

2006· article· en· W1912629087 on OpenAlexaff
T. Cornfield, Yu Gong, G. S. K. RAO, Liang Yang, William B. Gardner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMobile deviceGlobal Positioning SystemComputer scienceWearable computerALARMReal-time computingUploadEmbedded systemWearable technologyMobile computingSmartwatchEngineeringTelecommunicationsElectrical engineering

Abstract

fetched live from OpenAlex

The recent miniaturization of GPS receivers has made it possible to design a mobile "personal safety" system to help individuals avoid mapped landmines left over from previous conflicts. The handheld system continually compares the bearer's GPS-reported position with a compressed map of mined danger areas, sounding an alarm on approach. Two kinds of mobile devices are supported: a wearable wristwatch model for general alarm-only use, and a PDA model that also allows for recording and uploading of discovered mine locations. The mobile devices are integrated into a complete Internet-connected support system, with a central GIS landmine map repository and regional map distributors capable of updating mobile devices in their immediate locales. The data representations allow the mine maps to be stored in compressed form, and the algorithms for real-time proximity calculations are designed to minimize the computation, memory, and power requirements of the wearable mobile device, so as to reduce its cost, without sacrificing accuracy

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0430.016

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 head0.003
GPT teacher head0.172
Teacher spread0.170 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2006
Admission routes1
Has abstractyes

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