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Record W4384285069 · doi:10.4050/f-0075-2019-14625

Innolot: International Manufacturing Technology Development in Poland

2019· article· en· W4384285069 on OpenAlexaff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Applications
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsInvestment (military)Process (computing)Control (management)EngineeringBusinessEmerging technologiesEngineering managementManagementComputer sciencePolitical scienceEconomics

Abstract

fetched live from OpenAlex

The Innolot program represented some firsts for Sikorsky Aircraft and its Polish subsidiary, PZL Mielec. It was the first win of an international research and development program for Sikorsky as well as the largest manufacturing technology investment by either company. The conditions surrounding the program were also somewhat unique. The technologies and innovations developed existed in the United States and Europe, however they did not exist in Polish industry. The supply base was non-existent and little expertise in any of the technologies proposed existed outside of academia. Guidance and support from the United States was provided within a robust International Trade Compliance (ITC) process with special emphasis on avoiding inadvertent exports of manufacturing "know how". This program was a "first" in terms of technology and size for PZL Mielec, therefore an appropriate program management structure and culture was created and coached to insure proper control of projects replete with discovery. This paper explores the cultural as well as the technological aspects that led to a successful conclusion of the program in the spirit of the Vertical Flight Society: "…engineers, scientists and others working to advance vertical flight technology".

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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.409

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 head0.003
GPT teacher head0.178
Teacher spread0.176 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2019
Admission routes1
Has abstractyes

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