A Test of the Cosmological Principle with Reported Superlarge Structures
Bibliographic record
Abstract
The objective of this thesis is to investigate and study cross-correlation of large-scale structures as a test of the Cosmological Principle (CP). This begins with an explanation of the importance of CP to cosmology and astronomy, followed by a review of the concept of length scales as it pertains to CP, and a review of quasars and Gamma-ray Bursts (GRBs). The statistical methods and measurements used in this research will be explained in detail. The resulting statistics are explored for a Gamma-ray burst data set, two quasar data sets with positive and negative galactic latitude, and a data set comprised of quasar-GRB pairs to test for correlations. Statistically significant anomalies appearing in either method are discussed in detail, as well as specific analysis of signals that may relate to the previously reported structures. None of the signals discovered would indicate unusually large structures at a statistically significant level. The correlation study is likewise lacking in statistically significant signals, suggesting no apparent correlation between the GRB and quasar distributions. Lack of statistically significant structures in these distributions suggests that the Cosmological Principle holds. Sources of error are discussed. Potential future studies are laid out, including suggestions on how to increase the number of GRBs with redshift data.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".