Analisis Tingkat Kesulitan Keuangan Perusahaan Makanan dan Minuman Akibat Pandemi Covid-19
Bibliographic record
Abstract
Since the development of the Corona virus, which was first discovered towards the end of 2019 in Wuhan, Hubei Region, China, it has spread throughout the world because of its fast-spreading contagion. All the affected countries have adopted different strategies to reduce the rate of transmission of this infection, in particular small to large scope social restrictions. The implementation of these approaches in the long term will have a very real impact around the world, one of which is the decline in global financial movements, considering Indonesia. In this study, financial ratios were calculated for all data using financial ratios in the Modified Altman Z-Score model, Grover Score, Zmijewski. The results of the Altman Z-Score Altered model analysis on food and beverage companies during the Coronavirus pandemic, precisely in the 2020 quarter, food and beverage companies are expected to experience financial distress as many as 4 companies. The results of the calculation based on the analysis of the Grover Score model for food and beverage companies during the Coronavirus pandemic, to be precise, per quarter of 2020, the F&B companies that are expected to experience financial distress are 3 companies. The results of calculations based on the Zmijewski model for food and beverage companies during the Coronavirus pandemic, to be precise, per quarter of 2020, food and beverage companies that are expected to experience financial distress are 2 companies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".