Establishing new diffuse interstellar band correlations to identify common carriers
Bibliographic record
Abstract
ABSTRACT Observations from the Apache Point Observatory Catalog of Optical Diffuse Interstellar Bands (DIBs) were analysed to establish highly correlated pairs in terms of their equivalent widths (EWs) (r > 0.95), which importantly facilitate the identification of common carriers. A total of 154 846 possible DIB pairs were originally examined, yet only those with a sufficient number of sightlines (n > 9) that included EW uncertainties were subsequently investigated. The highest correlations for the resulting 56 893 DIB pairs are 6284.05–6203.58 Å (r = 0.990 ± 0.001), 6203.58–5780.64 Å (r = 0.986 ± 0.001), 6993.12–6269.89 Å (r = 0.984 ± 0.001), 6843.76–6792.51 Å (r = 0.984 ± 0.005), 6203.58–5487.64 Å (r = 0.983 ± 0.002), and 5061.50–4969.12 Å (r = 0.983 ± 0.009). The bands 5363.77, 5780.64, 6203.58, and 6284.05 Å appear most frequently. Novel relations linked to those DIBs and others warrant further research, in particular those pairs that involve one or both DIBs with low EWs (e.g. 5609.82, 6269.89, 6993.12, and 7224.16 Å). Numerous DIBs correlated with the prominent 4429.33 Å band were also discovered. The intriguing proposal of anionic hydrogen clusters as possible DIB carriers is also discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".