A Catalogue of High-Energy Observations of Galactic Supernova Remnants
Bibliographic record
Abstract
We present an update to the University of Manitoba's catalogue of high-energy observations of all known Galactic supernova remnants (SNRs), that we publicly released in 2012.We recall the rationale for our work, which aims at bridging the existing census of Galactic SNRs (primarily made at radio wavelengths) with the ever-growing and diverse observations of these objects at high-energies (in X-rays and γ-rays).We share some insights on the sustained and worldwide use of our online resource that is maintained regularly at www.physics.umanitoba.ca/snr/SNRcat.We give updated statistics on the SNR data collected to date, which provide a summary of our current view of Galactic SNRs.Finally, we present two upcoming extensions of our catalogue: one regarding a dedicated database for pulsar wind nebulae, the other related to the bilateral SNR subclass with an imaging component to the catalogue.We plan to keep this resource complete and up-to-date with high-energy observations, and continue to welcome feedback by the community.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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".