Bibliographic record
Abstract
Abstract The Green Bank Telescope (GBT) is the largest fully steerable radio telescope in the world and is one of our greatest tools for discovering and studying radio pulsars. Over the last decade, the GBT has successfully found over 100 new pulsars through large-area surveys. Here I discuss the two most recent—the GBT 350 MHz Drift-scan survey and the Green Bank North Celestial Cap survey. The primary science goal of both surveys is to find interesting individual pulsars, including young pulsars, rotating radio transients, exotic binary systems, and especially bright millisecond pulsars (MSPs) suitable for inclusion in Pulsar Timing Arrays, which are trying to directly detect gravitational waves. These two surveys have combined to discover 85 pulsars to date, among which are 14 MSPs and many unique and fascinating systems. I present highlights from these surveys and discuss future plans. I also discuss recent results from targeted GBT pulsar searches of globular clusters and Fermi sources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".