Bibliographic record
Abstract
We now have a broad understanding of the buildup of stellar mass in galaxies over much of cosmic time, however, a detailed understanding of how and where within galaxies the stellar mass grows remains elusive. Recent work with multi-wavelength HST imaging has shown an increased number of star-forming galaxies with bright clumps and knots of star formation during the epoch of cosmic star formation (z ~ 2). The formation and evolution of these clumps, often linked to the gravitational instability of the disk and the formation of bulges, is likely to be essential in the buildup of mass in galaxies. To constrain this evolutionary scenario, high resolution and multi-wavelength observation are needed for resolved colors and stellar population studies (eg. stellar mass, star formation rate, age), yet only a few fields have sufficient HST data to do so. We are using a new method called Finite Resolution Deconvolution on galaxies at 0.5 < z < 1.5 to deconvolve the multi-wavelength imaging within the COSMOS field and study the stellar mass within clumps. The total mass content of galaxies and their giant clumps are obtained by modelling the spectral energy distribution of 30 photometric bands in the optical and near-infrared, providing unprecedented constraints on the SEDs. Our preliminary results show that the fraction of clumpy galaxies is higher than previously measured at intermediate redshift, yet the total mass within the clumps is only modestly larger than the disk mass at similar 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 | 0.001 |
| 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".