Bibliographic record
Abstract
Abstract. Airports are not only important infrastructure for both civil and military use but also have significant impacts on socio-economic development and the built-up environment. OpenStreetMap (OSM) can be an essential data source for acquiring various airport elements, but few studies have investigated data quality. To fill this gap, this study aims to assess the quality (especially completeness) of airport data in OSM by comparing it with locations of airports acquired from the OurAirports platform. More precisely, the three different types (large, medium, and small) and the four different elements (runway, taxiway, apron, and terminal) of airports are assessed for over 40,000 airports worldwide. Results show that completeness varies depending on types, elements, and geographical regions. Specifically, 1) almost all large airports are complete; most medium airports are also complete; but most small airports are not complete. 2) The runway element is much more complete than the terminal element. 3) In most cases, completeness is relatively high in India, China, and Northern Africa but relatively low in Canada, the United States, Russia, and Australia, where the total number of airports is much larger. We conclude that most large and medium airports in OSM have been mapped well. The reasons for incomplete airport data in OSM and potential applications of OSM airport data are also discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".