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Colonel Robert Means Thompson and Noak Victor Hybinette: A Tale of Yin and Yang

2013· article· en· W2012812839 on OpenAlexaff
William F. Marcuson, D Baksa

Bibliographic record

VenueInternational Journal for the History of Engineering & Technology · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Studies in Latin America
Canadian institutionsVale (Canada)
Fundersnot available
KeywordsManagementSociologyArt historyLawArtPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Colonel Robert M. Thompson as pictured in and Victor Hybinette pictured in were co-stars of the nineteenth-century development of the North American nickel business. Thompson, the leading businessman, was accountable for development of the first commercially successful method of separating copper from nickel, the Orford Process. Hybinette, the leading technical expert, created major improvements to the Orford process, developed commercial smelting of nickel oxide to metal and commercialized electrolytic production of metallic nickel, the Hybinette Process. Yet, the two men were different — Thompson, a US Naval Academy graduate and Harvard-educated lawyer, urbane and sophisticated, clever in all matters of business and society; Hybinette, a brilliant Swedish émigré, best at molecules, equipment and equations, impulsive, moody, lacking in social skills but able to attract financial backers. But Hybinette’s success was never greater than when the Colonel was in control. The two men were truly Yin and Yang to each other — different but not opposites, complementary, light coming from dark and dark from light.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0110.001

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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2013
Admission routes1
Has abstractyes

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