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Record W2287706372 · doi:10.4271/2006-01-3587

Comparative Testing for Aftermarket (Secondary Item) Truck Brake Components by the US Army and Industry Partners

2006· article· en· W2287706372 on OpenAlexaff
Leo P. Miller, Carlos Agudelo

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2006
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsEngineering Link (Canada)
FundersU.S. Department of Defense
KeywordsTruckAutomotive engineeringBrakeAutomotive industryAeronauticsEngineeringManufacturing engineeringComputer scienceAerospace engineering

Abstract

fetched live from OpenAlex

Military and commercial fleets share many challenges, most of which are driven by Federal laws, regulations, and policies. The US military has one of the largest and most varied wheeled vehicle fleets in the world with a broad range of vehicle weights and types, most over 10,000 lbs GVW. These vehicle brake systems include disc and drum, using mechanical, hydraulic, and air actuation systems. The US military buys vehicle systems only, not the individual components or subsystems other than for spare parts (a.k.a. Secondary Items). In addition, brake shoes are bought as assembled units only and not as separate brake blocks or lining. The objective of the Government project presented in this paper was to provide a decision-making tool so that the responsible engineering authority could make a reasoned decision on the acceptability of alternative spare parts and sources through a Government-approved standardized off-vehicle testing process. Subject paper presents the background, different test plans, test procedures, and the main workflow for 1) potential offerors interested in pursuing a brake component (secondary item) supply contract with the US Military, 2) comparative testing for non-standard vehicle configurations (overloaded), and 3) research and development (R&D) of alternative systems. The output of the Government's effort was the development of the “ATPD 2354” specification and its pre-planned replacement, a DODISS-approved Federal Test Standard (FTS) in MIL-STD-962 format.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.020
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.004

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 head0.028
GPT teacher head0.279
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2006
Admission routes1
Has abstractyes

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