Liquidity variations and variability cohesiveness with revenue and profitability: A case of Saudi energy sector companies
Bibliographic record
Abstract
Liquidity refers to the paying ability of the business organization while profitability assesses the profit earning capacity of the business organization. The liquidity of the business organization can be bifurcated into two based on time i.e., short-term and long-term liquidity. The short-term liquidity reveals the operational efficiency while long-term liquidity refers to the financial capability to repay the long-term debts of the business organization. The short-term paying ability is the management of the working capital or efficient management of the current assets and current liabilities. The current assets and current liabilities are directly related to the revenue of the business and further affected by the profitability, indirectly. The long-term paying ability or financial health of the business organization is reflected by the debts and equity ratio. The energy sector of Saudi Arabia is a prominent sector and contributes to the economy progressively. The study is based on secondary data and reveals the long-term and short-term liquidity variations and the cohesiveness of long-term and short-term liquidity with the revenue and profitability of energy sector companies. The study reveals the significant variations in the short-term and long-term liquidity and cohesiveness between the revenue, profitability, and short-term and long-term liquidity of the energy sector 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".