Air Transport Demand Forecast to Making the Regional Aviation Sustainable in Northeast of Brazil
Bibliographic record
Abstract
Both researchers and Government Agencies see aviation as an important driver for regional development and national integration. Thus, this sector has been a matter of concern for the government who has ways to stimulate the aero activity. The Regional Aviation Development Program (PDAR) has been currently under development implemented in Brazil. This program foresees public investments in airport infrastructure and operational subsidies for airlines to enhance the sector operation and increase the number of locations served by regional aviation. This paper presents a model for estimating passenger demand potential through multiple linear regression to cover the great majority of the federative units (states) of Pernambuco, Paraíba, Rio Grande do Norte, Ceará, and Piauí in the northeast of Brazil. Subsequently, localities are suggested to optimize the resources of the PDAR, and we concluded that it is likely that there are regions with higher demand potential than some regions, which are already served by the airlines. Hence, we assumed that by strategically directing investments to specific localities, companies operate without subsidies, which in turn can be directed to airlines used to integrate the country. This making regional aviation more sustainable leading development to isolated localities, and thus efficiently contributing to reducing the Brazilian social inequality.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".