Randomly generated problems for the complexity resolution problem in a multi sector planning context
Bibliographic record
Abstract
All the randomly generated problems in this data set involve a number A of aircraft passing through a square multi-sector area (MSA) of side 600 km. This MSA is composed of four square adjacent sectors of side 300 km. The aircraft use four different flight levels that belong to the same MSA. The aircraft trajectories are randomly generated in such a way that all aircraft are either flying from bottom to upper MSA borders, or from left to right borders. Taking the origin at the bottom left corner of the MSA, the distance between the first waypoint and the origin is randomly generated using the continuous uniform distribution U[75 km, 595 km]. Each trajectory is composed of three waypoints located on the MSA edges. The first waypoint is located on either the bottom or the left MSA border. The other two waypoints are generated randomly along the opposing sector borders using a uniform distribution. The cruise speeds of the aircraft are randomly generated using the continuous uniform distribution U[458 knots, 506 knots]. The time at which the aircraft enters the MSA follows the continuous uniform distribution U[20 min, 90 min]. The flight level used for each trajectory is randomly generated using a discrete uniform distribution U{1, K}. A constant flight level is used by 90% of the aircraft. The others undergo one flight level change at the internal boundary. For these aircraft, the second flight level is randomly generated using U{1, K} while excluding the first sector flight level.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".