Bibliographic record
Abstract
Abstract People working on stellar populations can look forward to an exciting decade ahead. Investigations of stellar populations lie at the heart of the science cases being used to justify the development of upcoming telescopes and emerging instrumentation technologies. Examples abound, but I will focus on three case studies: (1) Wide field astronomy with upcoming ground-based and space-based survey facilities; (2) Adaptive optics, which has the potential to revolutionize our understanding of stellar populations in both nearby and distant galaxies; (3) The James Webb Space Telescope, which may well extend the reach of stellar population work to encompass the full range of the star-forming history of the Universe. However, most of these developments will require extensive advance preparation in order to be used effectively. The time to start that preparation is now (if not yesterday). Three areas which need urgent development are highlighted in these proceedings: (1) We need a wide-field high-resolution spectroscopic capability to augment wide-area imaging surveys; (2) We need a set of AO-friendly extragalactic deep fields in order to exploit upcoming AO-fed instrumentation; and (3) Existing tools for population synthesis modeling need to be extended in order to incorporate the effects of dust. Because the physics of dust creation and destruction is so complicated and uncertain, the latter capability sounds almost impossibly hard to develop, but in this talk I will argue that some simple approaches already exist that allow dust to be injected rather naturally into population synthesis models. I will show a concrete example where incorporation of dust into spectral synthesis models allows one to detect and characterize rate of formation of circumstellar disks at high redshifts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".