ASKAP Project AS102: WALLABY Pilot Survey DR2 - High Resolution Catalogue
Bibliographic record
Abstract
These are value-added data products from running both the SoFiA 2 source-finding pipeline and the WALLABY kinematic modelling proto-pipeline on selected high-resolution targets. It contains a catalogue containing both the source and kinematic model properties and data products from the source finding pipeline and kinematic modelling photo-pipeline. The source data products for each detection are: HI data cube; SoFiA source mask; maps of moment 0, 1 and 2; map showing the number of spectral channels contributing to each pixel in the moment 0 map; integrated HI spectrum. The kinematic modelling products are full-resolution and spectrally-smoothed data and model cubes; difference cube; kinematic model parameters; rotation curve; surface density profile. Note that not all detections have an accompanying kinematic model. The data release is described in an accompanying paper which provides details on the observations, data reduction, data quality and associated data products. Users of the data are requested to include references to the data release paper (Murugeshan et al. 2024, in prep.) and the WALLABY survey description paper (Koribalski et al. 2020, Ap&SS, 365, 118) in any publication resulting from their analysis.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 0.116 |
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".