Covid-19'un Türkiye'nin Dış Ticaret Taşıma Türlerine Etkisinin İncelenmesi
Bibliographic record
Abstract
The emergence of Covid-19 and the fact that it became a pandemic in a short time has created a shock effect in the international arena. This situation has forced the country and international organizations to take strict measures in the economic and social field in order to prevent the spread of the pandemic. The international supply chain has been severely affected by the measures taken, and therefore international trade and economy have also suffered. As a result of this situation, national and international economies have shrunk. This contraction in the economy and the measures implemented caused the differentiation of transportation types, which are one of the most basic components of international trade. This study was conducted to examine the effects of Covid-19 on the modes of transportation used in Turkey's export transportation. In this context, the export data for the years 2017-2021 obtained by e-mail from the Turkish Statistical Institute were analyzed by numerical methods and tables were created. Obtained tables were analyzed and interpreted. As a result, the Covid-19 pandemic has significantly reduced Turkey's total exports. However, in this process, it has been determined that Covid-19 has a positive effect on rail transport and the amount of rail transport has increased. In addition, road transport was not affected by the pandemic as much as total exports, and the amount of transport decreased proportionally. However, air and sea transportation have been seriously affected by this process. In this study, it was determined that the effects of the pandemic were felt seriously in the second quarter of 2020 and that it started to decrease as of the fourth quarter of the year as a result of the new strategies implemented in this process.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".