GHIGLS: H I MAPPING AT INTERMEDIATE GALACTIC LATITUDE USING THE GREEN BANK TELESCOPE
Bibliographic record
Abstract
This paper introduces the data cubes from GHIGLS, deep Green Bank Telescope surveys of the 21-cm line emission of HI in 37 targeted fields at intermediate Galactic latitude. The GHIGLS fields together cover over 1000 square degrees at 9.55' spatial resolution. The HI spectra have an effective velocity resolution about 1.0 km/s and cover at least -450 < v < +250 km/s. GHIGLS highlights that even at intermediate Galactic latitude the interstellar medium is very complex. Spatial structure of the HI is quantified through power spectra of maps of the column density, NHI. For our featured representative field, centered on the North Ecliptic Pole, the scaling exponents in power-law representations of the power spectra of NHI maps for low, intermediate, and high velocity gas components (LVC, IVC, and HVC) are -2.86 +/- 0.04, -2.69 +/- 0.04, and -2.59 +/- 0.07, respectively. After Gaussian decomposition of the line profiles, NHI maps were also made corresponding to the narrow-line and broad-line components in the LVC range; for the narrow-line map the exponent is -1.9 +/- 0.1, reflecting more small scale structure in the cold neutral medium (CNM). There is evidence that filamentary structure in the HI CNM is oriented parallel to the Galactic magnetic field. The power spectrum analysis also offers insight into the various contributions to uncertainty in the data. The effect of 21-cm line opacity on the GHIGLS NHI maps is estimated.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".