Bibliographic record
Abstract
Interview in six sessions in January and February 2001 with John D. Baldeschwieler, J. Stanley Johnson Professor and professor of chemistry, emeritus, in the Division of Chemistry and Chemical Engineering. Dr. Baldeschwieler received his bachelor's in chemical engineering from Cornell in 1956 and his PhD in 1959 from UC Berkeley.\nHe begins by recalling his childhood and early education in Cranford, N.J. His father, an analytical chemist, emigrated from Switzerland and his mother from Manitoba. He matriculated at Cornell in 1951 and enrolled in ROTC during the Korean War. Recalls summer work at Los Alamos and graduate school at Berkeley 1956-1959; his thesis on infrared spectroscopy, with George Pimentel; interest in instrument building. After six months' active duty at Aberdeen Proving Ground, Md., joins the Harvard faculty; becomes a consultant for Aberdeen. Early work with nuclear magnetic resonance (NMR). Invited to Stanford as associate professor in 1965, where he works on electron cyclotron resonance, in connection with Varian Associates.\nJoins Army Scientific Advisory Panel; works on "people sensors" during the Vietnam War. Appointed to PSAC (President's Science Advisory Committee); discussion of defoliant Agent Orange. Becomes deputy director of the Office of Science and Technology in 1970, during first Nixon administration; takes a leave from Stanford and moves to Washington, D.C. Recalls the debates on biological warfare and on whether or not to build the SST (supersonic transport). Recollections of various figures in the Nixon administration. Resigns from government in December 1972 and goes to work at the National Cancer Institute for six months.\nInvited to become chairman of Caltech's Division of Chemistry and Chemical Engineering. Arrives in 1973, during Harold Brown's presidency; discusses his close relationship with Brown and his reorganizing of chemistry division, of which he remains chairman until 1978. Meanwhile, consults for Monsanto and Merck and becomes involved in the US-Soviet joint scientific program. Visits the USSR in the early 1970s. Travels with Glenn Seaborg on the first chemistry delegation to China in 1978. Work on binding liposomes to cancer cells in the late '70s; forms company called Vestar to commercialize the technique as a diagnostic tool. Collaboration with the City of Hope. Discussion of patenting and licensing of discoveries made at Caltech and of proposed high-tech corridor for Pasadena.\nHe concludes the interview by remarking on his children and stepchildren and their work, and he lists the various technology companies he has helped to establish.\n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".