MétaCan
Menu
Back to cohort
Record W4416875954 · doi:10.37665/smvmgay66187

0201 Technology Implementation Strategy for CEMs

2003· article· W4416875954 on OpenAlex
Reggie Malli

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

VenueSMTA International · 2003
Typearticle
Language
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsPhoenix Technologies (Canada)
Fundersnot available
KeywordsKey (lock)ElectronicsService (business)Emerging technologiesAdvanced manufacturingManufacturing operationsManufacturingCustomer service

Abstract

fetched live from OpenAlex

ABSTRACT Typically, the introduction and implementation of new technologies in the Contract Electronics Manufacturing (CEM) environment is driven by the existing or new customer demand, however, the CEMs must recognize that adherence to this strategy can increase the risk of losing an existing or a potential customer. On the other hand, in today’s economy of tighter margins, shrinking market-share and diminishing demand, it is economically impractical to invest in equipment upgrades and extensive experiments beforehand and hope that a customer will come along with the requirement for the new technology that the CEM or Electronics Manufacturing Service (EMS) provider has already put in place. In this article, our approach towards the introduction and implementation of the 0201 technology in manufacturing will be discussed. We feel that the best approach is to be prepared to introduce 0201 technology into manufacturing by investing in the training of key individuals, identifying the equipment capabilities, upgrading requirements and documenting a formal road-map.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.001

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 head0.044
GPT teacher head0.332
Teacher spread0.287 · 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