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Record W2983569001 · doi:10.1021/cen-09437-notw9

Geography plays role in fate of two deals

2016· article· en· W2983569001 on OpenAlexaboutno aff
Michael McCoy

Bibliographic record

VenueC&EN Global Enterprise · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Geography and Geographical Thought
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyEconomic geography

Abstract

fetched live from OpenAlex

The chemical industry is as global as they come, but location still matters. An international combination of industrial gases companies was just called off, in part because of disagreement about the location of certain jobs. Meanwhile, a merger of two fertilizer companies is going ahead, eased by the fact that the firms are both Canadian. The gases merger, first revealed last month, was to combine U.S.-based Praxair and Germany’s Linde. The new company would have had a global industrial gases market share of about 40% and annual sales of close to $28 billion, making it by far the world’s largest gases firm. At the time, outside analysts said obtaining antitrust clearances to create the company would present hurdles. But in the end it was questions about who would do what and where that killed the deal. “While the strategic rationale of a merger has been principally confirmed, discussions about details,

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.032
Scholarly communication0.0150.013
Open science0.0010.007
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0130.002

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 head0.007
GPT teacher head0.283
Teacher spread0.276 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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