Regionális repülőterek határon átnyúló együttműködésének lehetőségei Pécs és Eszék példáján keresztül = The possibility of cross-border cooperation of regional airports through the example of Osijek and Pécs
Bibliographic record
Abstract
2023. január 1-jén Horvátország csatlakozott a schengeni övezethez, így megszűnt a személyek határellenőrzése a közös, horvát-magyar határszakaszon is. Ennek köszönhetően könnyebbé vált a közlekedés Baranya, illetve Eszék-Baranya megyék között, ami tovább fog fokozódni az M6-os autópálya 20 km-es zárószakaszának – 2024 első negyedévében tervezett – forgalomba helyezésével. Ebben a helyzetben új lehetőségek nyílnak az említett megyék együttműködésében rejlő, kölcsönös előnyök kiaknázására, amelynek ékes példája lehetne a Pécs és Eszék mellett található regionális repülőterek közötti, határon átnyúló együttműködés kialakítása. A tanulmány a két létesítmény sajátosságait és az eddigi történetük fontos tanulságait járja körül, egy sikeres kooperáció pedig hasznos tapasztalatokkal szolgálhat a többi, jelenleg hasonló problémákkal küzdő regionális repülőtér számára. | On 1 January 2023, Croatia joined the Schengen area, which means that border controls on persons will no longer be carried out at the common Croatian-Hungarian border. This will facilitate traffic between the counties of Baranya and Osijek-Baranya, which will be further enhanced by the opening of the 20 km final section of the M6 motorway, scheduled for the first quarter of 2024. In this situation, new opportunities will open up for exploiting the mutual benefits of cooperation between these counties, such as the cross-border cooperation between the regional airports near Pécs and Osijek. The study will explore the specificities of the two facilities and the important lessons learned from their history so far. A successful cooperation could provide useful lessons for other regional airports facing similar problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.013 |
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".