A brief history of cosmology
Bibliographic record
Abstract
Some highlights of the history of modern cosmology and the lessons to be learned from the successes and blind alleys of the past are described.This heritage forms the background to the lectures and discussions at this Second Carnegie Centennial Symposium, which celebrates the remarkable contributions of the Carnegie Institution in the support of astronomical and cosmological research. 1.1M. S. Longair rather exact science (see Longair 2001).Without these developments, none of us would be celebrating the achievements of modern cosmology at this symposium.Before getting down to the history, let me contribute a personal appreciation of Andrew Carnegie's philanthropy.He gave away $350M of his fortune of $400M to charitable causes.Among the more remarkable of these was the founding of about 3000 libraries worldwide, including five in my home town of Dundee in Scotland, only about 30 miles from Dunfermline, Carnegie's birthplace.Here is a quotation from the Carnegie Libraries of Scotland web-site:When the library was officially opened on 22 October 1908, [Charles] Barrie [a former Lord Provost] was asked to perform the
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.018 |
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".