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Record W2810073576 · doi:10.1520/jte20170342

Walk-through Metal Detector Testing and the Need to Emulate Natural Body Motion

2018· article· en· W2810073576 on OpenAlexaff
Donald R. Larson, Nicholas G. Paulter, Nikolaus F. Troje

Bibliographic record

VenueJournal of Testing and Evaluation · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsQueen's University
Fundersnot available
KeywordsMotion (physics)DetectorTrajectoryObject (grammar)Motion detectionComputer scienceComputer visionConstant (computer programming)Line (geometry)SimulationArtificial intelligencePhysicsMathematicsGeometry

Abstract

fetched live from OpenAlex

Abstract The detection performance of a walk-through metal detector (WTMD) is affected not only by the electromagnetic properties and size and shape of the test objects, but potentially also by the type of motion of the test object through the portal of the WTMD. This motion, it has been argued, can contribute to the uncertainty in detecting threat objects being carried through the WTMD. Typical laboratory-based testing uses a robotic system, or similar, to push a test object through the portal with a trajectory that is a straight line and has a constant velocity. This testing, although reproducible and accurate, does not test for those trajectories the are representative of natural body motion. We report the effects of nonrectilinear trajectories on the detection performance of WTMDs.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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 head0.081
GPT teacher head0.359
Teacher spread0.278 · 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 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

Citations3
Published2018
Admission routes1
Has abstractyes

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