Bibliographic record
Abstract
A unique individual with a fascinating life story, Ivar Giaever is a scientist who won the Nobel Prize in Experimental Physics in 1973. In his own words, Giaever relates an absorbing tale of how important luck and good fortune have been in shaping his life. He narrates the story of an ordinary childhood in Norway and an unremarkable undergraduate career at university. After finishing his engineering degree, he served in the Norwegian army and married his childhood sweetheart, Inger Skramstad. His desire to make a better life for his new family led Ivar to Canada and then to the United States. Even without an advanced degree in a scientific field, Ivar was given the opportunity to work with cutting-edge scientific researchers at General Electric R&D in Schenectady, New York. While there, he completed his PhD at Rensselaer Polytechnic Institute — one of the nation's oldest technological universities. His work on superconductivity led to worldwide recognition and the Nobel Prize. This memoire is more than the story of an accomplished, world-renowned scientist: it is an engaging reminiscence of an independent, highly creative thinker and problem solver who loves games and puzzles, skiing and windsurfing, and time with friends and family. Dr Ivar Giaever's fascinating story intertwines his views on the nature of science, scientific processes, contemporary issues such as global warming, and the great benefits the Nobel Prize has afforded him. Written with humor and often tongue-in-cheek, "I am the Smartest Man I know" is one man's meditation on science, intellectual inquiry, and life itself.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.014 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.009 | 0.042 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".