Bibliographic record
Abstract
The Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst project (CHIME/FRB) has begun detecting FRBs at an unprecedented rate. This allows for the first time the study of FRB properties in a large, coherent population. However, the CHIME/FRB detection pipeline is subject to many subtle selection effects. Thus, the detection sample from CHIME/FRB is not representative of the true FRB population. In order to correct for the biases introduced during CHIME/FRB event detection, a synthetic pulse injection system was developed which allows for the injection of a large population of simulated FRBs into the live telescope datastream. By injecting pulses drawn from a realistic FRB population, the detection signal-to-noise ratio (SNR) of synthetic pulses could be compared across pulse input parameters. Injected pulses were calibrated to physical energy units (Jy ms) in real time, and the pulse position in the telescope field-of-view was simulated, providing an authentic representation of detecting real FRBs on the sky. The final set of injections and corresponding detections will be reweighted such that the output distribution matches what has actually been observed by CHIME/FRB. This will begin to correct for the telescope's selection effects
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".