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Record W2121057022 · doi:10.1109/autest.2010.5613557

Logistics support system; harmonization enhances multi-national support

2010· article· en· W2121057022 on OpenAlexaff
Bruce E. Scott, Anthony J. Minei

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering and Test Systems
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsOriginal equipment manufacturerAerospaceEngineering managementService (business)ProcurementAutomatic test equipmentEngineeringHarmonizationTest (biology)OutsourcingAeronauticsManufacturing engineeringSystems engineeringOperations researchComputer scienceBusinessOperating systemMarketingReliability engineering

Abstract

fetched live from OpenAlex

This Paper examines the evolution of the Harmonization approach for the LM-STAR®Multi-National Support. Critical to the Lockheed Martin Corporation's approach to winning the JSF/F35 contract in 2002 was developing a common logistics approach. A strategy was devised for ATE Harmonization whereas all Original Equipment Manufacturers (OEMs) would utilize a common ATE subset solution knowing one day programs developed on the subset solution would be required to execute on a full up solution for Depot support, etc. Lockheed Martin Simulation, Training & Support (LMSTS) was already executing the Consolidated Automated Support System (CASS) and some of its early PBL efforts, Contractor Logistic Support (CLS) efforts, and the Consolidated Service Pool Program (CSP) and realized how a common solution was beneficial to the US Navy and other Foreign Military users of CASS. A test envelope was established using the CASS as a baseline. Several OEM/supplier meetings were held and the test envelope was expanded to handle additional OEM test requirements. The first station built was a full-up superstation with the entire test envelope supported. From this point, each OEM working with their Lockheed Martin Aerospace buyer, filled out a requirements needs as well as a quantity and need date. From these requirements LMSTS developed various configurations and delivered 62 System Design & Development (SDD) testers to OEMs in the United States, United Kingdom, and other JSF partnering countries. LMSTS is now delivering 16 more LM-STARs®in Low Rate Initial Production (LRIP) 3 to the OEMs and in LRIP4 LMSTS will deliver 21 stations to OEMS and three to the Depot.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0060.006
Open science0.0020.008
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.004

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 head0.017
GPT teacher head0.231
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

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 designNot applicable
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
Published2010
Admission routes1
Has abstractyes

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