Characterising the efficiency of the hierarchical clustering method
Bibliographic record
Abstract
The Hierarchical Clustering Method (HCM) is the de-facto clustering algorithm in the search for groups of “family” asteroids that were formed due to the breakup of a larger parent body. In this work, we feed to the HCM a number of synthetic asteroid families at different positions in the main belt and of different dynamical ages. We then measure how effectively the algorithm can recover the family members, while minimising the inclusion of non-family members. The three metrics of “accuracy”, “precision”, and “recall” were used to characterise the HCM efficiency. The most important factor in the HCM’s ability to cluster families was found to be the relative number density between the family and the asteroid background it is situated in. We compared families to the background in a parameter space defined by proper orbital elements semi-major axis, eccentricity, and inclination (a, e, i), and for families approximately four times denser than the background ∼ 50% of the original family is recovered independently of age. However, to reach these relative densities for families older than 2 Gyr it was necessary to artificially reduce the synthetic background population. We conclude that older families would be all but undetectable in the real main belt, using the HCM. • As asteroid families age, they disperse into and mingle with background asteroids. • Efficiency is primarily decreased through missed family bodies, and not interlopers. • Families older than 2 Gyr are significantly harder to find. • This leads to the phenomena of asteroid family halos, and scarcity in older families.
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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 | 0.021 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".