Bibliographic record
Abstract
Airports recently were invited to share overviews of airport sustainability (or green) projects with Airport Magazine. Compiled highlights discuss issues such as Leadership in Energy and Environmental Design (LEED) status; rainwater collection; plumbing; HVAC systems; runway and taxiway lighting and repaving; aircraft rescue and fire fighting facilities; sustainability master plan development; airport procurement requirements; organics composting; onsite electric and compressed natural gas vehicle use; terminal design; centralized air use; land use; and recycling. Airports profiled include: Austin Straubel International (Wisconsin); Barkley Regional (Kentucky); Grand Canyon National (Arizona); Ithaca Tompkins Regional (New York); Minneapolis-St. Paul International (Minnesota); Monterey Peninsula (California); Randolph County (Indiana); Redmond Municipal (Oregon); San Diego International (California); Seattle-Tacoma International (Washington); South Bend Regional (Indiana); Tallahassee Regional (Florida); and Wichita Mid-Continent (Kansas). Information is also provided for Los Angeles World Airports, which owns and operates three California airports (Los Angeles International; Los Angeles/Ontario International; and Van Nuys), and the Truckee Tahoe, California, Airport District. Color photographs complement the article.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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; both teacher heads agree on what is shown here.
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".