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.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".