Bibliographic record
Abstract
Gamma-ray astronomy probes the most extreme and violent events in the Universe. This young, but rapidly blooming, field has witnessed a giant leap in the past decade thanks to the advance of space- and ground-based instruments allowing us to study the non-thermal Universe in the GeV–TeV energy range. The major arrays of atmospheric Cherenkov telescopes (VERITAS, MAGIC and H.E.S.S.) are now providing us with an unprecedented view of the very high-energy sky powered by almost 200 TeV astrophysical sources. The 6th International Symposium on High-Energy Gamma-Ray Astronomy (Gamma2016) has gathered scientists from around the world to discuss major observational and theoretical aspects of the field. This review summarizes the Galactic science results presented at Gamma2016, and provides an outlook for the future. This is an observational overview, with focus on supernova remnants (SNRs), pulsar wind nebulae (PWNe) and binaries: after all SNRs and PWNe form the largest population of TeV sources in our Galaxy.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".