Mitigating calibration errors from mutual coupling with time-domain filtering of 21 cm cosmological radio observations
Bibliographic record
Abstract
Acknowledgements: This work was supported in part by the Italian Ministry of Foreign Affairs and International Cooperation, grant number ZA23GR03. GB acknowledges support from the Ministry of Universities and Research (MUR) through the PRIN project ‘Optimal inference from radio images of the epoch of reionization’. NK acknowledges support from NASA through the NASA Hubble Fellowship grant #HST-HF2-51533.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. MGS acknowledges support from the South African Radio Astronomy Observatory and National Research Foundation (grant no. 84156). OMS’s research was supported by the South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation (grant No. 81737). AL and RP acknowledge support from the Trottier Space Institute, the Canadian Institute for Advanced Research (CIFAR) Azrieli Global Scholars program, a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant, a NSERC/Fonds de recherche du Québec-Nature et Technologies NOVA grant, the Canada 150 Research Chairs Program, the Sloan Research Fellowship, and the William Dawson Scholarship at McGill.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".