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Record W4386960460 · doi:10.1002/prep.202300118

Effects of encapsulation of manganin gauges on the pressure profiles from shock initiation experiments

2023· article· en· W4386960460 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

VenuePropellants Explosives Pyrotechnics · 2023
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsMD Precision (Canada)
FundersNatural Science Foundation of Anhui ProvinceAnhui University of Science and TechnologyAnhui University
KeywordsManganinExplosive materialMechanicsMaterials scienceShock (circulatory)Shock wavePressure measurementAcousticsCalibrationThermodynamicsPhysicsChemistry

Abstract

fetched live from OpenAlex

Abstract The Lagrangian test can measure the shock initiation process of explosives for different incident shock pressures. The manganin gauges record pressure histories for several DNAN‐based melt‐cast explosives. The encapsulation of the gauges results in three types of characteristic signals: (1) a step signal on the low‐pressure shock front, (2) a flatten Von Neuman's (VN's) spike on the high‐pressure shock front, and (3) a V‐shape signal behind the shock front. To reveal the mechanism underlying these characteristic signals, a one‐dimensional Lagrangian hydrocode, was used to simulate the propagation of a sustained shock in both ideal and non‐ideal systems. The simulation results show that the impedance mismatch between encapsulation material and explosives is the main reason for the three characteristic signals. The calibration of shock initiation model should take the encapsulation material into account so as to determine shock initiation model parameters accurately.

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 categoriesnone
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 score0.004
Threshold uncertainty score0.364

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 head0.016
GPT teacher head0.212
Teacher spread0.196 · 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