Experimental cosmic statistics - I. Variance
Bibliographic record
Abstract
Counts-in-cells are measured in the $\tau$CDM Virgo Hubble Volume simulation. This large N-body experiment has 10^9 particles in a cubic box of size 2000 h^{-1} Mpc. The unprecedented combination of size and resolution allows for the first time a realistic numerical analysis of the cosmic errors and cosmic correlations of statistics related to counts-in-cells measurements, such as the probability distribution function P_N itself, its factorial moments F_k and the related cumulants $\xiav$ and S_N's. These statistics are extracted from the whole simulation cube, as well as from 4096 sub-cubes of size 125 h^{-1}Mpc, each representing a virtual random realization of the local universe. The measurements and their scatter over the sub-volumes are compared to the theoretical predictions of Colombi, Bouchet & Schaeffer (1995) for P_0, and of Szapudi & Colombi (1996, SC) and Szapudi, Colombi & Bernardeau (1999a, SCB) for the factorial moments and the cumulants. The general behavior of experimental variance and cross-correlations as functions of scale and order is well described by theoretical predictions, with a few percent accuracy in the weakly non-linear regime for the cosmic error on factorial moments. (... more in paper >...)
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".