THE PERILS OF CLUMPFIND: THE MASS SPECTRUM OF SUBSTRUCTURES IN MOLECULAR CLOUDS
Bibliographic record
Abstract
We study the mass spectrum of substructures in the Perseus Molecular Cloud Complex traced by 13 CO(1–0), finding that dN / dM ∝ M −2.4 for the standard Clumpfind parameters. This result does not agree with the classical dN / dM ∝ M −1.6 . To understand this discrepancy, we study the robustness of the mass spectrum derived using the Clumpfind algorithm. Both two- and three-dimensional Clumpfind versions are tested, using 850 μm dust emission and 13 CO spectral-line observations of Perseus, respectively. The effect of varying threshold is not important, but varying stepsize produces a different effect for two- and three-dimensional cases. In the two-dimensional case, where emission is relatively isolated (associated with only the densest peaks in the cloud), the mass spectrum variability is negligible compared to the mass function fit uncertainties. In the three-dimensional case, however, where the 13 CO emission traces the bulk of the molecular cloud (MC), the number of clumps and the derived mass spectrum are highly correlated with the stepsize used. The distinction between "two dimension" and "three dimension" here is more importantly also a distinction between "sparse" and "crowded" emission. In any "crowded" case, Clumpfind should not be used blindly to derive mass functions. Clumpfind's output in the "crowded" case can still offer a statistical description of emission useful in intercomparisons, but the clump-list should not be treated as a robust region decomposition suitable to generate a physically meaningful mass function. We conclude that the 13 CO mass spectrum depends on the observations resolution, due to the hierarchical structure of the MC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".