What is a GMC? Are observers and simulators discussing the same star-forming clouds?
Bibliographic record
Abstract
As both simulations and observations reach the resolution of the star-forming molecular clouds, it becomes important to clarify if these two techniques are discussing the same objects in galaxies. We compare clouds formed in a high-resolution galaxy simulation identified as continuous structures within a contour, in the simulator's position–position–position (PPP) coordinate space and the observer's position–position–velocity space (PPV). Results indicate that the properties of the cloud populations are similar in both methods and up to 70 per cent of clouds have a single counterpart in the opposite data structure. Comparing individual clouds in a one-to-one match reveals a scatter in properties mostly within a factor of 2. However, the small variations in mass, radius and velocity dispersion produce significant differences in derived quantities such as the virial parameter. This makes it difficult to determine if a structure is truly gravitationally bound. The three cloud types originally found in the simulation in Fujimoto et al. are identified in both data sets, with around 80 per cent of the clouds retaining their type between identification methods. We also compared our results when using a peak decomposition method to identify clouds in both PPP and PPV space. The number of clouds increased with this technique, but the overall cloud properties remained similar. However, the more crowded environment lowered the ability to match clouds between techniques to 40 per cent. The three cloud types also became harder to separate, especially in the PPV data set. The method used for cloud identification therefore plays a critical role in determining cloud properties, but both PPP and PPV can potentially identify the same structures.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".