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Record W4386299424 · doi:10.57017/jaes.v17.2(76).01

Some Financial Aspects of the Corporate Groups that Operate in Food Distribution and Specialized Retail in the COVID-19 Pandemic. Outline of the Determined Financial Indicators of Jerónimo Martins, SGPS, S.A.

2022· article· en· W4386299424 on OpenAlexaff
Michał Mrozek

Bibliographic record

VenueJournal of Applied Economic Sciences (JAES) · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsDistribution (mathematics)PortugueseDiversification (marketing strategy)CommodityEconomicsFinanceEconomyBusinessMarketingMathematics

Abstract

fetched live from OpenAlex

The paper regards the selected financial indicators of the Jerónimo Martins, SGPS, S.A.: LFL Growth, Employment, Net Sales and Services, EBITDA Mg, Long Term Borrowings, Short Term Borrowings, and Total Borrowings. The fundamental aim of the paper is the assessment of the selected financial indicators of the Jerónimo Martins, SGPS, S.A. in terms of the COVID-19 pandemic. The following research problems were put forward: What is the diversification of the financial indicators of the Jerónimo Martins, SGPS, S.A. as a Portuguese corporate group that operates in food distribution and specialized retail? Which of the researched financial aspects of the Jerónimo Martins, SGPS, S.A has the highest, the middle, and the lowest level in 2017-2021? In the theoretical part of the paper was depicted the portfolio of Jerónimo Martins, SGPS, S.A. The studies were carried out: documentation, statistical, comparative, and dynamics analysis. The results showed that the Jerónimo Martins, SGPS, S.A. in terms of the COVID-19 pandemic as a Portuguese corporate group that operates in food distribution and specialized retail had different tendencies. The inference process took place in a deductive way.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.259
Teacher spread0.187 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2022
Admission routes1
Has abstractyes

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