Bibliographic record
Abstract
In his 1917 “On Quantum Theory of Radiation ” paper Albert Einstein introduced the concept of amplification of radiation through stimulated emission with coherence. This concept had not been applied in practice until 1952 when Joseph Weber, followed by Townes, Basov and Prokhorov, developed the theory and started working on the construction of masers. The first maser was demonstrated by Townes ’ team in 1953. Many researchers began thinking about making an “optical maser”, but the transition from centimeter to nanometer waves posed a problem. Well funded researchers in the USA and Soviet Union put their efforts into making an “optical maser”. In May 16, 1960 an unknown and underfunded researcher, Theodore Harold Maiman, won the race and demonstrated a fully functional ruby laser. The scientific world was astonished by its simplicity and elegance. Maiman’s short paper describing the invention appeared immediately in Nature magazine. This invention caused an avalanche of new laser developments followed by the growing number of applications in almost all fields of our lives. Ted Maiman died in 2007 in Vancouver, just 13 days before the 47th anniversary of the invention of the laser. The intention of this paper is to focus on the life of the scientific maverick and great man.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".