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Prospective of Product Development and Improved Production Processes

2021· article· en· W3158366081 on OpenAlexaff
O.S.I. Fayomi, I.G. Akande, Sunday O. Oyedepo, Israel Dunmade, Daniel O. Aikhuele, Desmond Eseoghene Ighravwe

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsMount Royal University
Fundersnot available
KeywordsNew product developmentProcess managementProduct (mathematics)BusinessLean manufacturingSix SigmaProduction (economics)Lean Six SigmaManufacturing engineeringOperations managementComputer scienceEngineeringMarketingMathematics

Abstract

fetched live from OpenAlex

Abstract Product development is an integral part of an organization that plans to stay relevant and competitive in today’s world. Several companies have research and development sections, which are most times responsible for developing new products. Still, there are other processes such as technology development, technology acquisition, capacity acquisition, and project management in which product development can be achieved. Improved product development processes such as lean manufacturing, six sigma, and a hybrid of the two known as lean six sigma (LSS) are platforms that enhance product development, which helps organizations remain relevant.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.465

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 communication0.0000.001
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 head0.009
GPT teacher head0.187
Teacher spread0.178 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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