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Record W4388624841 · doi:10.1021/cen-10137-buscon2

Industry profits evaporate in Europe

2023· article· en· W4388624841 on OpenAlexaboutno aff
Alex Scott

Bibliographic record

VenueC&EN Global Enterprise · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessQuarter (Canadian coin)CITESEarningsAgricultural economicsGermanCommerceChemical industryFinanceEconomicsEngineeringGeography

Abstract

fetched live from OpenAlex

Many of Europe’s biggest chemical producers—including the German firms BASF, Covestro, Evonik Industries, and Lanxess—have reported declining sales and net losses for the third quarter. Europe’s chemical sector is now firmly in cost-cutting mode, and some companies are closing manufacturing plants because of ongoing soft demand for their products. BASF, still the world’s largest chemical company, recorded a loss of $264 million for the third quarter, compared with earnings of $962 million in the year-earlier period, and sales of $16.2 billion, down 28%. The German major cites considerably lower prices for products sold by its materials, chemicals, and surface technologies businesses and lower sales volumes across the board. BASF announced measures to cut costs by about $215 million annually, adding to plans to cut costs by more than $750 million per year by 2027. The firm plans to reduce its capital investments over the next 5 years by about $4.3

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.026
GPT teacher head0.331
Teacher spread0.305 · 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 teacher head, not a consensus.

Study designObservational
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
Published2023
Admission routes1
Has abstractyes

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