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Bibliographic record
Abstract
The authors show how Kendall's tau can be adapted to test against serial dependence in a univariate time series context.They provide formulas for the mean and variance of circular and non-circular versions of this statistic, and they prove its asymptotic normality.They present also a Monte Carlo study comparing the power and size of a test based on Kendall's tau to that of competing procedures based on alternative parametric and nonparametric measures of serial dependence.In particular, their simulations indicate that Kendall's tau outperforms Spearman's rho in detecting first-order autoregressive dependence, despite the fact that these two statistics are asymptotically equivalent. R SUM Les auteurs montrent comment le tau de Kendall peut tre adapt pour tester la prsence de dpendance srielle dans une srie chronologique univarie.Ils dterminent l'esprance et la variance de versions circulaire et non-circulaire de cette statistique et en dmontrent la normalit asymptotique.Une tude de Monte-Carlo leur permet aussi de comparer le seuil et la puissance d'un test fond sur cette statistique celle de tests concurrents s'appuyant sur d'autres mesures paramtriques et non paramtriques de dpendance srielle.Leurs simulations indiquent entre autres que le tau de Kendall dtecte plus facilement la prsence de dpendance autorgressive de premier ordre que le rho de Spearman, bien que ces deux statistiques soient asymptotiquement quivalentes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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