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Record W4416877944 · doi:10.37665/smfmzzn99009

Material Control for Lead-Free Manufacturing

2004· article· W4416877944 on OpenAlex
François Monette

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 · 2004
Typearticle
Language
FieldEngineering
TopicFlexible and Reconfigurable Manufacturing Systems
Canadian institutionsCentre Intégré de Santé et Services Sociaux de la Gaspésie
Fundersnot available
KeywordsProcess (computing)Production (economics)Task (project management)Component (thermodynamics)Control (management)Production lineKey (lock)Production control

Abstract

fetched live from OpenAlex

ABSTRACT The upcoming transition to Lead-Free electronics assembly has been a subject of much research and discussion lately. To manage this important transition requires a significant effort in many areas. The challenge does not stop when the new process and materials have been selected and qualified. The key issue that remain is that of managing the material logistics. A successful transition will require a significant collaborative effort between production, engineering, procurement, and a large number of component suppliers and distributors. During this process one area that should not be overlooked is the actual production floor. After all this is where all the different materials come together to make the finished product. The assembly line is where the largest number of people are involved and the complexity of their task translates in the highest risk of errors. This paper focuses on the practical considerations of managing the materials on the production floor during the transition to Pb-free.

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)0.000
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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.221
Teacher spread0.211 · 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