A Framework for Modeling the Evolution of Young Stellar Objects
Bibliographic record
Abstract
Abstract Measuring properties of young stellar objects (YSOs) is necessary for probing the pre-main-sequence evolution of stars. As YSOs exhibit complex geometry, measurement generally entails comparing observed radiation to template populations of radiative-transfer model YSO spectral energy distributions (SEDs). Due to uncertainty on the precise mechanics of star formation, the properties inferred for YSOs using these models often depend strongly on the assumed accretion history. We develop a framework for predicting observable properties of YSOs that is agnostic to the underlying accretion history, enabling comparison between theories. This framework links a set of radiative-transfer SEDs with protostellar evolutionary tracks to create models of evolving YSOs. Unlike previous works, we directly relate evolution models to observables through theoretical physical parameters rather than through intermediate, observationally derived analogs. We make flux predictions for YSOs corresponding to stars with birth masses from 0.2 to 50 M ⊙ during their accretion phase following isothermal-sphere, turbulent-core, and competitive accretion histories, showing that these histories may be observationally distinguished by examining the 100 μm and 3 mm fluxes of a YSO. We discuss the impact of dust models and parameter ranges on the output of radiative-transfer simulations through a comparison to another SED model grid. We quantify the degree of confusion between YSO Stages and Classes across a wide range of physical scenarios; for each, we calculate confusion matrices that enable inference of the number of objects of a given Stage from an observed population. Finally, we critically examine the physical significance of various literature Stage and Class definitions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".