CHOPIN: Methods and Results of the Fourth Global Trajectory Optimization Competition
Bibliographic record
Abstract
In the Winter of 2009, the CHOPIN (Canada, HOlland, jaPan orbit Investigator Network) team of the Japan Aerospace Exploration Agency (JAXA) participated in the fourth Global Trajectory Optimization Competition (GTOC4). This time, the objective was driven by the question: "How to maximize the relevance of a rendezvous mission to a given NEA by visiting the largest set of intermediate asteroids?" For the competition, the spacecraft had to be launched from Earth with a hyperbolic excess velocity of up to 4 km/s. Then, using electric propulsion, the spacecraft had to flyby a maximum number of asteroids from a given list and rendezvous with a last one within 10 years. The mission was constrained to have a launch window between 2015 and 2025, and the spacecraft wet mass was assumed to be 1000 kg, with a spacecraft specific impulse of 3000 s and a thrust level constrained to 0.135 N. Finally, each asteroid could only be visited once. The performance index to be maximized was the number of asteroids and the final mass of the spacecraft. In this paper, we go over the methods used, results obtained and lessons learned.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".