Bibliographic record
Abstract
Abstract We discuss some of the claims that have been made regarding the statistics of fast radio bursts (FRBs). In an earlier Letter, we conjectured that flicker noise associated with FRB repetition could show up in non-cataclysmic neutron star emission models, like supergiant pulses. We show how the current limits of repetition would be significantly weakened if their repeat rate really were non-Poissonian and had a pink or red spectrum. Repetition and its statistics have implications for observing strategy, generally favouring shallow wide-field surveys, since in the non-repeating scenario survey depth is unimportant. We also discuss the statistics of the apparent latitudinal dependence of FRBs, and offer a simple method for calculating the significance of this effect. We provide a generalized Bayesian framework for addressing this problem, which allows for direct model comparison. It is shown how the evidence for a steep latitudinal gradient of the FRB rate is less strong than initially suggested and simple explanations like increased scattering and sky temperature in the plane are sufficient to decrease the low-latitude burst rate, given current data. The reported dearth of bursts near the plane is further complicated if FRBs have non-Poissonian repetition, since in that case the event rate inferred from observation depends on observing strategy.
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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 | 0.008 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".