Bibliographic record
Abstract
Extraordinary claims demand extraordinary evidence. ( Anon ) In the past it may have been true that Stephen Senn's (2003) analogy was right. Paraphrasing his words for the sake of moderate language: scientists regarded statistics as the one-night stand: the quick fix, avoiding long-term entanglement . This analogy is no longer apt. Statistical procedures now drive many if not most areas of current astrophysics and cosmology. In particular the currently understood nature of our Universe is a product of statistical analysis of large and combined data sets. Here we briefly describe the scene in three areas dominating definition of the current model of the Universe and its history. The three areas inextricably tie together the shape and content of the Universe and the formation of structure and galaxies, leading to life as we know it. While these sketches are not reviews, we show by cross-referencing how frequently our preceding discussions play in to current research in cosmology. The galaxy universe The story of galaxy formation since 1990 is based on two premises. Firstly, it was widely accepted that the matter content in the Universe is primarily cold and dark – CDM prevails. The recognition of dark matter was slow, despite Zwicky (1937) demonstrating its existence via the cosmic virial theorem. The measurements of rotation curves of spiral galaxies (e.g. Rubin et al. , 1980) convinced us.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.074 | 0.023 |
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".