Associations between Scattering Screens and Interstellar Medium Filaments
Bibliographic record
Abstract
Abstract Pulsar scintillation can be used to measure small-scale structure in the Galaxy, but little is known about the specific interstellar medium (ISM) features that cause scintillation. We searched for ISM counterparts to all scintillation screens for which absolute distances and scattering orientations have been measured—a sample of 12 pulsars and 22 screens. For one pulsar, PSR J0737–3039A, we reanalyze its scintillation screen and find evidence for a highly anisotropic screen. Comparisons with tracers of ionized media did not find any new associations. Instead, for seven of the pulsars analyzed, aligned H i filaments are seen for at least one of their screens, for a total of 12 out of 22 screens. This result seems unlikely to be due to chance: Comparing with random trials, we estimate a likelihood of finding 12 or more screens with aligned emission by chance of only 0.004%. Estimating the significance of the amount of aligned emission (in standard deviations over the mean), the probability of finding as much observed aligned emission by chance is larger, at 1.7%, but still indicative of a real correlation. Since H i filaments are preferentially associated with cold neutral gas, and thus unlikely to cause scintillation, this may indicate that both the filaments and the screens are aligned preferentially by the same mechanisms, such as magnetic fields or shocks.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".