MIGHTEE-HI: The H I emission project of the MeerKAT MIGHTEE survey
Bibliographic record
Abstract
We present the H I emission project within the MIGHTEE survey, currently being carried out with the newly commissioned MeerKAT radio telescope. This is one of the first deep, blind, medium-wide interferometric surveys for neutral hydrogen (H I ) ever undertaken, extending our knowledge of H I emission to z = 0.6. The science goals of this medium-deep, medium-wide survey are extensive, including the evolution of the neutral gas content of galaxies over the past 5 billion years. Simulations predict nearly 3000 galaxies over 0 < z < 0.4 will be detected directly in H I , with statistical detections extending to z = 0.6. The survey allows us to explore H I as a function of galaxy environment, with massive groups and galaxy clusters within the survey volume. Additionally, the area is large enough to contain as many as 50 local galaxies with H I mass < 10 8 M ⊙ , which allows us to study the low-mass galaxy population. The 20 deg 2 main survey area is centred on fields with exceptional multi-wavelength ancillary data, with photometry ranging from optical through far-infrared wavelengths, supplemented with multiple spectroscopic campaigns. We describe here the survey design and the key science goals. We also show first results from the Early Science observations, including kinematic modelling of individual sources, along with the redshift, H I , and stellar mass ranges of the sample to date.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.007 | 0.004 |
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".