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Record W2113980502 · doi:10.1109/ccst.1999.797903

User performance testing of the Perimitrax buried cable sensor

2003· article· en· W2113980502 on OpenAlexaff
M. Maki, Cherice Hill, Charles R. Malone

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsSenstar (Canada)
Fundersnot available
KeywordsEngineeringCoaxial cableSoftware deploymentTelecommunicationsComputer scienceElectrical engineeringSoftware engineering

Abstract

fetched live from OpenAlex

Beginning in the 1970's buried cable sensors have been used extensively to provide perimeter intrusion detection for a wide range of military, government and industrial facilities around the world. In 1998, the U.S. Army Engineer Research and Development Center (USAERDC), in partnership with Senstar-Stellar Corporation, conducted testing of the Perimitrax buried cable sensor. Perimitrax is a new product manufactured by Senstar-Stellar that features an advanced leaky coaxial cable and digital signal processor for intruder detection and includes an integrated command and control system for alarm display, maintenance, and diagnostics. The Perimitrax technology was first introduced at the 1996 Carnahan Conference. The purpose of this testing, sponsored by the U.S. Air Force, Force Protection Command and Control Systems Program Office, was to determine the performance characteristics of the installed Perimitrax product, when deployed in a wide range of deployment mediums. Testing was conducted at an ERDC facility that featured three types of soil and three types of pavement in which Perimitrax sensor cables were installed and tested. This paper reviews features and characteristics of the sensor, outlines the test protect undertaken, and presents a summary and interpretation of results in order to guide the user in its successful application.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.401

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 head0.014
GPT teacher head0.194
Teacher spread0.179 · 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 designSimulation or modeling
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

Citations3
Published2003
Admission routes1
Has abstractyes

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