Bibliographic record
Abstract
Low- to intermediate-mass (∼ 0.8−8 M⊙) evolved stars contribute significantly to the chemical enrichment of the interstellar medium in the local Universe. It is therefore crucial to accurately measure the mass return in their final evolutionary stages. The Nearby Evolved Stars Survey (NESS) is a large multi-telescope project targeting a volume-limited sample of ∼ 850 stars within 3 kpc in order to derive the dust and gas return rates in the solar neighbourhood, and to constrain the physics underlying these processes. We present an initial analysis of the CO-line observations, including detection statistics, carbon isotopic ratios, initial mass-loss rates, and gas-to-dust ratios. We describe a new data reduction pipeline for homogeneity, which we use to analyse the available NESS CO data from the James Clerk Maxwell Telescope, measuring line parameters and calculating empirical gas mass-loss rates. We present the first release of the available data on 485 sources, one of the largest homogeneous samples of CO data to date. Comparison with a large combined literature sample finds that high mass-loss rate and especially carbon-rich sources are over-represented in literature, while NESS is probing significantly more sources at low mass-loss rates, detecting 59 sources in CO for the first time and providing useful upper limits on non-detections. CO line detection rates are 81% for the CO(2−1) line and 75% for CO(3−2). The majority (82%) of detected lines conform to the expected soft parabola shape, while eleven sources show a double wind. Calculated mass-loss rates show power-law relations with both the dust-production rates and expansion velocities, up to a mass-loss rate saturation value ∼ 5 × 10−6 M⊙ yr−1. Median gas-to-dust ratios of 250 and 680 are found for oxygen-rich and carbon-rich sources, respectively. Our analysis of CO observations in this first data release highlights the importance of our volume-limited approach in characterizing the local AGB population as a whole.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".