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Record W2902283730

TiO2 prices hit Q1 AkzoNobel revenues

2018· article· eo· W2902283730 on OpenAlexaboutno aff
William Clarke

Bibliographic record

VenueIndustrial Minerals · 2018
Typearticle
Languageeo
FieldEconomics, Econometrics and Finance
TopicEconomic and Technological Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueAgricultural economicsCurrencyRaw materialCommerceBusinessFellQuarter (Canadian coin)Product (mathematics)Natural resource economicsEconomicsMonetary economicsFinanceGeographyChemistry
DOInot available

Abstract

fetched live from OpenAlex

Despite the implementation of product price rises, the company has been struggling with rising raw material costs with headwinds expected to continue in 2018. The higher cost of raw materials such as titanium dioxide and a fall in shipments of marine and oil and gas coatings hit AkzoNobel’s first-quarter revenues, it said. Although the average selling price for its coatings products was up by 3% year on year in the first three months of 2018, revenues fell 8% to €2.16 billion ($2.64 billion) thanks to currency effects and a 3% drop in volumes. “Volumes in marine coatings continued to be affected by the slowdown in new-build activity, despite some recovery in other segments,” AkzoNobel said. “Protective coatings volumes decreased due to fewer oil & gas projects.” The company’s net income from continuing operations fell by 6% - the high price of raw materials, such as titanium dioxide, continued to weigh on profits. “Headwinds experienced during 2017 - including higher raw materials costs and adverse effects from foreign currencies - are projected to continue in 2018, especially [at] the start of the year,” AkzoNobel said.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.006

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 head0.187
GPT teacher head0.267
Teacher spread0.080 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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