Editorial: The Sun seen with the Atacama Large mm and sub-mm array (ALMA)—First results1
Bibliographic record
Abstract
et al. comes next, where the results on the temperature structure of the chromosphere, the chromospheric Figure 1. ALMA images of the Sun at 1.3 mm (band 6). Left: Full disk image obtained by scanning with a single dish; the spatial resolution is 30 . Right: High resolution (1 ) image of the region around the sunspot, obtained in interferometric mode with mosaicking. Both images were obtained on December 18, 2015, during the commissioning period and were processed by the authors for better visibility of the disk features.network and spicules are presented, followed by the research article by Tarr et al., which compares the 23 3 mm brightness temperature with the Hα line width in the weak solar network. (Rutten, 2017), with brilliant ideas and acute but constructive criticism. He will be remembered by the 45 entire solar physics community, and we dedicate this Research Topic to his memory.This work makes use of the following ALMA data: ADS/JAO.ALMA.2011.0.00020.SV. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC 48 (Canada), MOST and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".