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Record W2077720415 · doi:10.1117/12.2028783

Multispectral and hyperspectral advanced characterization of soldier's camouflage equipment

2013· article· en· W2077720415 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
Typearticle
Languageen
FieldEngineering
TopicInfrared Target Detection Methodologies
Canadian institutionsTelus (Canada)
Fundersnot available
KeywordsCamouflageMultispectral imageHyperspectral imagingContext (archaeology)Computer scienceRemote sensingInfraredVNIRSystems engineeringArtificial intelligenceEngineeringGeologyOpticsPhysics

Abstract

fetched live from OpenAlex

The requirements for soldier camouflage in the context of modern warfare are becoming more complex and challenging given the emergence of novel infrared sensors. There is a pressing need for the development of adapted fabrics and soldier camouflage devices to provide efficient camouflage in both the visible and infrared spectral ranges. The Military University of Technology has conducted an intensive project to develop new materials and fabrics to further improve the camouflage efficiency of soldiers. The developed materials shall feature visible and infrared properties that make these unique and adapted to various military context needs. This paper presents the details of an advanced measurement campaign of those unique materials where the correlation between multispectral and hyperspectral infrared measurements is performed.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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