Assigning airport gates during a pandemic using tolerance graphs
Bibliographic record
Abstract
Tolerance graphs, introduced by M. C. Golumbic and C. L. Monma in 1982, are a generalization of interval graphs. In this paper, we propose an application of coloring of tolerance graphs together with machine learning, in particular supervised learning, in solving problems of airport gate assignment during a pandemic of an airborne disease. The idea is to minimize a contact between passengers at the gates, in order to slow down a spread of the disease. This application includes calculating the chromatic number of a graph and finding a clique of a given size in the graph. As a result, we obtain the minimum number of gates needed under the given assumptions and the corresponding gate assignment. Further, we propose an application of list coloring, a generalization of a coloring, of tolerance graphs in solving these problems. Besides the theoretical approach, the corresponding algorithms are given. The algorithms developed may take into account several parameters, such as the number of passengers on a flight, the number of newly infected people per 1000 inhabitants. A similar approach can be taken for classroom assignment during a pandemic, scheduling meetings, etc.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".