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Record W1543540093 · doi:10.1007/0-306-47673-8_2

Design for Manufacture

2006· book-chapter· en· W1543540093 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueKluwer Academic Publishers eBooks · 2006
Typebook-chapter
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

We generally think of mass production as a uniquely twentieth-century phenomenon. However, its evolution can be traced back much further. The explosion in printed books, following Johannes Gutenberg’s fifteenth-century development of the Korean invention of movable type, had an impact on human society of heroic proportions. Precursors of modern mass-production, based on the specialization of labour and the use of specialized machinery to ensure a high degree of uniformity, can be traced to the eighteenth century. Writing in The Wealth of Nations in 1776, Adam Smith used the manufacture of pins to exemplify the improvement in productivity resulting from the utilization of uniform production techniques. Today, every conceivable sort of commodity is mass-produced. Pills, paints, pipes, plastics, packages, pamphlets and programs are mixed, extruded, poured, forged, rolled, stamped, molded, glued, printed, duplicated and dispatched worldwide on an immense daily scale. The most successful modern products are an amalgamation of many disciplines, years of experience, careful execution, rigorous production control and never-ending refinement. In no other industry is the cross-disciplinary matrix so tightly woven, and the number of interacting elements so incredibly high, as in the semiconductor business. Reaching back to Gutenberg, and drawing on the principles of photography pioneered by Daguerre in the 1830s (embracing optics, lens-making, photosensitive films and chemistry), transistors are defined by a process of lithography, which is essentially printing. But what eloquent printing this is! A 200-mm silicon wafer has a useful area of about a little less than a page of this book containing some 400 words of text, equivalent to perhaps 16,000 bits. However, when divided into chips the size of a modest microprocessor, today containing about 50 million transistors, through perhaps 20 successive layers of printing and processing each wafer generates some 10 billion devices in a single mass-produced entity. In a production lot containing 40 such wafers, some 400 billion tiny objects are manufactured in a single batch. Multiply this by the daily manufacture of integrated circuits worldwide, and it will be apparent that the number of transistors that have been produced Barrie Gilbert

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), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.207
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
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 head0.020
GPT teacher head0.210
Teacher spread0.190 · 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