Statistical and Financial Analysis of Georgian Railway`s Main Performance Indicators in 2006-2019
Bibliographic record
Abstract
Due to the geopolitical location, Georgia can become the center for Caucasus transport-logistics; partly it still performs this function. The purpose of this research paper is to study and analyze the financial-economic and statistical position of the main indicators of the Georgian Railway Holding. Based on all the above mentioned, we have set out the tasks of the research: Statistic analysis of the value added created by the Railway Industry in the Georgian economy years 2006-2019, Determination of correlation between the general indicators of JSC "Georgian Railway" and factors operating on it, Comparative analysis of the financial indicators of the Georgian railway in the post soviet space. Data was taken from the Georgian Railway Information Technology Agency. We observed the sensitivity of cargo movement in the region. The correlation between the general indicators of JSC "Georgian Railway" and its operating factors are also reflected in the study. Despite the small portion of the railway in the country's GDP, its role in the socio-economic development of the country is great. The average annual geometric growth of the EBITDA of regionals railways is decreasing. This reduction is caused by general economic shocks in region and slowing of economic growth. However, it is worth mentioning that the results of Georgian Railway compared with the other countries are only 4% reduction. This means that the reduction of shipping of oil and dry cargo by the Georgian Railways in recent times is caused by external factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".