The MaGICC volume: reproducing statistical properties of high-redshift galaxies
Bibliographic record
Abstract
We present a cosmological hydrodynamical simulation of a representative volume of the Universe, as part of the Making Galaxies in a Cosmological Context (MaGICC) project. MaGICC uses a thermal implementation for supernova and early stellar feedback. This work tests the feedback model at lower resolution across a range of galaxy masses, morphologies and merger histories. The simulated sample compares well with observations of high-redshift galaxies (z ≥ 2) including the stellar mass–halo mass (M⋆-Mh) relation, the galaxy stellar mass function (GSMF) at low masses (M⋆ < 5 × 1010 M⊙) and the number density evolution of low-mass galaxies. The poor match of M⋆-Mh and the GSMF at high masses (M⋆ ≥ 5 × 1010 M⊙) indicates that supernova feedback is insufficient to limit star formation in these haloes. At z = 0, our model produces too many stars in massive galaxies and slightly underpredicts the stellar mass around L⋆ mass galaxy. Altogether our results suggest that early stellar feedback, in conjunction with supernova feedback, plays a major role in regulating the properties of low-mass galaxies at high redshift.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".