Feasibility study of using Muon observations for extreme space weather early warning (final report)
Bibliographic record
Abstract
This project is motivated by the need for improved protection of Canadian critical infrastructure from solar disturbances. The feasibility study examines the possibility of using measurements of muons produced by cosmic rays (CR) to provide advanced warning of approaching solar disturbances. A literature review and workshops with invited specialists were the important part of work. These identified the need for a Canadian muon detector to fill the coverage gap existing in global network. Two types of tasks were undertaken: theoretical investigations of the interaction of cosmic rays with solar disturbances performed by the Lead Department, NRCan; and review of the existing technology and development of a test detector, done by a Contractor, Physics Department at Carleton University. The study identified two types of precursors associated with interaction of the CR with solar disturbance. The physics-based transport equation for CR has been analysed and diffusion model was validated. Two types of technology for building detectors were assessed, a test muon detector has been built and prototype data were analysed. The detailed design specifications and recommendations (roadmap) for proto-operational developments are provided.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".