Bibliographic record
Abstract
Through a series of meetings and surveys, professionals involved in the design, engineering, construction, and maintenance of airports throughout the United States and Canada have identified the key issues currently facing airport professionals, and trends in the industry that must be dealt with in the coming years. Among the current issues identified are altered route structures and the growth of airline hubs; continuing growth in the airline passenger and air cargo markets; the downsizing of the staff of the Federal Aviation Administration (FAA) and other government agencies; an increased focus on public-private partnerships; and growth in the global economy and a changing worldwide political environment. Among the major trends that will impact airports in the future are increasing airport delays, which affect not just the airport involved but many other airports throughout the system; the development of so-called superjet aircraft that will require wider, smoother pavements and that may not be accommodated at many existing airports; changes and improvement to technology, including GPS-guided flight control systems and other navigational developments; and environmental concerns, including the need to comply with noise, air, and water pollution regulations. A peer group of airport professionals meets every few months at a given airport to discuss these issues and try to find mutual solutions.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".