Bibliographic record
Abstract
The Standard Model of Particle Physics (SMPP) is an enormously successful description of high-energy physics, driving ever more precise measurements to find "physics beyond the standard model", as well as providing motivation for developing more fundamental ideas that might explain the values of its parameters. Simultaneously, a description of the entire three-dimensional structure of the present-day Universe is being built up painstakingly. Most of the structure is stochastic in nature, being merely the result of the particular realization of the "initial conditions" within our observable Universe patch. However, governing this structure is the Standard Model of Cosmology (SMC), which appears to require only about a dozen parameters. Cosmologists are now determining the values of these quantities with increasing precision to search for "physics beyond the standard model", as well as trying to develop an understanding of the more fundamental ideas that might explain the values of its parameters. Although it is natural to see analogies between the two Standard Models, some intrinsic differences also exist, which are discussed here. Nevertheless, a truly fundamental theory will have to explain both the SMPP and SMC, and this must include an appreciation of which elements are deterministic and which are accidental. Considering different levels of stochasticity within cosmology may make it easier to accept that physical parameters in general might have a nondeterministic aspect. PACS Nos.: 98.80.–k, 98.80.Bp, 98.80.Es, 12.60.–i
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".