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Record W2898894897

Company results: : Higher lithium prices boost FMC revenues in Q1

2017· article· en· W2898894897 on OpenAlexaboutno aff
Martim Facada

Bibliographic record

VenueIndustrial Minerals · 2017
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueEarningsQuarter (Canadian coin)Lithium (medication)Point (geometry)Agricultural economicsEconomicsBusinessMathematicsFinanceGeography
DOInot available

Abstract

fetched live from OpenAlex

Lithium earnings up 45%; Higher prices for hydroxide: Revenues increase 9% FMC Corp saw earnings from its lithium segment jump to $22m in Q1 2017, from $15m from Q4 2016. Meanwhile revenue from the segment rose 9% to $66m. Higher prices and improved product mix in Q1 offset the impact of lower volume and higher costs on earnings, according to the US-based producer. Outlook In the same announcement, FMC increased its lithium outlook for the full year by $10m at the mid-point versus the prior forecast. The segment revenue for full year 2017 is forecasted to be in a range of $325-365m and the full-year segment earnings are anticipated to be between $100m and $120 m. This revised forecast for full year segment earnings represents an increase of over 55% at the mid-point compared to the prior year while second quarter earnings are expected to range between $19m and $23m, an increase of 27% at the mid-point compared to the prior year quarter. This article from the June issue of Industrial Minerals magazine was first published online on 3 May 2017.

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)0.002
metaresearch head score (Gemma)0.003
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.160
Threshold uncertainty score0.536

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0040.001
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.1600.053

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 head0.103
GPT teacher head0.312
Teacher spread0.210 · 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
Published2017
Admission routes1
Has abstractyes

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