Understanding Galactic Star Formation with Next Generation X-ray\n Spectroscopy and Imaging
Bibliographic record
Abstract
This white paper is motivated by open questions in star formation, which can\nbe uniquely addressed by high resolution X-ray imaging and require an X-ray\nobservatory with large collecting area along good spectral resolution. A\ncomplete census of star-forming regions in X-rays, combined with well matched\ninfrared (IR) data, will advance our understanding of disk survival times and\ndissipation mechanisms. In addition, we will be able to directly observe the\neffects of X-ray irradiation on circumstellar grain growth to compare with\ngrain evolution models in both high- and low-UV environments. X-rays are native\nto stars at all phases of star formation and affect planet-forming disks\nespecially through flares. Moreover, X-rays trace magnetic fields which weave\nthrough the flares, providing a unique, non-gravitational feedback mechanism\nbetween disk and star. Finally, the bright X-ray emission emanating from hot\nplasma associated with massive stars can have large scale impacts on the\ntopology of star-forming regions and their interface with the interstellar\nmedium (ISM).\n
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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 | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".