MétaCan
Menu
Back to cohort
Record W4308801768 · doi:10.1177/00405175221135869

Fabric armour erosion using a buried charge ejecta analog

2022· article· en· W4308801768 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

VenueTextile Research Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicTraumatic Ocular and Foreign Body Injuries
Canadian institutionsCarleton University
Fundersnot available
KeywordsArmourMaterials scienceAbrasiveAbrasion (mechanical)Explosive materialComposite materialErosionParticle (ecology)Shaped chargeTextileDebrisPolymerLayer (electronics)Geology

Abstract

fetched live from OpenAlex

The threat posed by buried explosive charges and their explosive dispersal of environmental debris is associated with a variety of debilitating injuries. The dispersion profile of such threats includes particulate in a broad range of particle length scales, which are more destructive to ballistic fabrics than larger particles alone. Smaller particles are initially dispersed at higher speeds, degrading armour fabrics prior to the arrival of the larger penetrating particulate. In the present work, we present a low-cost industrial abrasion test method to investigate the abrasive wear in neat and polymer-coated ballistic fabrics to determine the level of degradation of the fabrics under the abrasive load. These fabrics were subjected to the impingement of an abrasive jet with average particle velocity of 187 ± 20 m/s. The results showed evidence of significant degradation and failure within the ballistic fabrics, that would certainly influence their subsequent ballistic performance. The addition of polymer coatings was able to reduce the abrasive degradation of the fabrics. The failure modes of the polymer composites are similarly described. This methodology shows promise as a means of armour material screening for this particular threat.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
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 score0.642
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.153
GPT teacher head0.423
Teacher spread0.270 · 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