Cosmic Magnetism
Bibliographic record
Abstract
Magnetic fields are involved in every astrophysical process on every scale: from planetary and stellar interiors to neutron stars, stellar wind bubbles and supernova remnants; from the interstellar medium in galactic disks, nuclei, spiral arms and halos to the intracluster and intergalactic media. They are involved in essentially every particle acceleration process and are thus fundamental to non-thermal physics in the Universe. Key questions include the origin of magnetic fields, their evolution over cosmic time, the amplification and decay processes that modify their strength, and their impact on other processes such as star formation and galaxy evolution. Astrophysical plasmas provide a unique laboratory for testing magnetic dynamo theory. The study of magnetic fields requires observations that span the wavelength range from radio through infrared, optical, UV, X-ray, and gamma-ray. Canada has an extremely strong record of research in cosmic magnetism, and has a significant leadership role in several ongoing and upcoming global programs. This white paper will review the science questions to be addressed in the study of cosmic magnetic fields and will describe the observational and theoretical opportunities and challenges afforded by the telescopes and modelling capabilities of today and tomorrow.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".