Bibliographic record
Abstract
In 2015, the Korean Government’s Ministry of Land, Infrastructure and Transport (MOLIT) launched a feasibility study for increasing airport capacity in the Youngnam Region of Korea, the southeast quarter of the country. The Ministry appointed a consortium formed by the Korea Transportation Institute (KOTI) and ADPI (Aéroport de Paris Ingenierie, member of the Aéroports de Paris Group) to develop the methodology for deciding at which site airport expansion should take place. In the framework of that work, the Korean Government requested that a roundtable be organised by the International Transport Forum to review the methodology developed for site selection and the criteria employed with a view to ensuring that the exercise undertaken for the Korean Government reflects current international best practice. This report is a product of this roundtable, organised in Paris in February 2016. The review is based on examination of methodologies used for selecting airport expansion sites in four different ITF member countries: Australia, Japan, Portugal and the United Kingdom. This report is part of the International Transport Forum’s Case-Specific Policy Analysis series. These are topical studies on specific issues carried out by the ITF in agreement with local institutions.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.031 |
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".