Indicator Random Variables in Traffic Analysis and the Birthday Problem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper proposes using collisions of Pareto random variables in traffic analysis and in generating fictitious network traffic that follows various Pareto distributions. Pareto distributions are commonly found in network statistics, but the distributions may be truncated or overlapping, thus making it hard to estimate their sample parameters. Therefore, this paper investigates methods of computing parameters of binned collisions of Pareto random variables. This paper explores an indicator variable approach to analyzing collisions of Pareto random variables. These collisions are initially modeled by the birthday problem or paradox and then they are extended to understand independence of collisions. This paper's use of indicator variables simplifies the calculation of higher moments for binned collisions of Pareto random variables
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it