An examination of large-scale galactic effects on molecular cloud properties in NGC 628: the significant impact of tidal effects from neighbouring material on the evolution of molecular clouds
Bibliographic record
Abstract
ABSTRACT The physical factors that influence the development of molecular cloud’s density contrast are connected to those that affect star formation in the galaxy. For NGC 628 (M74), the proportion of high- and low-density contrast clouds initially increases with the distance to the galactic centre ($R_{G}$) and then keeps relatively stable. Spiral arms, bubbles, and magnetic fields are not responsible for the variations in density contrast observed among molecular clouds. The effects of shear and tides calculated from the galactic rotation curve consistently decrease as $R_{G}$ increases, and the shear effect can be neglected. We further studied the tidal effects of the neighbouring material on each cloud using the tidal tensor analysis and the pixel-by-pixel computation, after combining molecular gas, atomic gas, and stellar mass surface density maps. When $R_{\rm G} \lt $ 4 kpc, the tidal strengths derived from the pixel-by-pixel computation decrease as $R_{\rm G}$ increases, and then remains relatively constant when $R_{\rm G} \gt $ 4 kpc. This aligns well with the dependence of the proportion of high- and low-density contrast clouds on $R_{\rm G}$. Therefore, the tidal effects of neighbouring material have a significant impact on the development of molecular cloud’s density contrast. A key factor contributing to the low star formation rate in the galactic centre is the excessive tidal influences from neighbouring material on molecular clouds, which hinder the gravitational collapse within these clouds, resulting in low density contrasts. The tidal effects from neighbouring material may also be a significant contributing factor to the slowing down of a pure free-fall gravitational collapse for gas structures on galaxy-cloud scales revealed in our previous works by velocity gradient measurements.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".