Chief of the Physical Chemistry Division. Soon after that, he joined the newly established
Bibliographic record
Abstract
Office of Standard Reference Data (OSRD) at NBS as a program manager in the areas of thermodynamics, thermophysics, and colloid and surface chemistry. White’s work on thermodynamic databases brought him into contact with related projects in other countries, which in turn led to his participation in several international organizations, such as the International Union of Pure and Applied Chemistry (IUPAC), and the Committee on Data for Science and Technology (CODATA). White was the US interface with thermodynamic data activities in the Soviet Union under the program for scientific collaboration established by the Nixon administration in the early 1970s. During several visits to the Soviet Union, he worked out valuable collaborations on developing thermodynamic databases. In the 1970s and early 1980s, OSRD sponsored and White managed a new formulation of the properties of water and steam by L. Haar and J.S. Gallagher at NBS, together with G.S. Kell, National Research Council, Canada: The NBS/NRC Steam Tables. It was based on more extensive data over larger pressure and temperature ranges than the formulations then existing. White played a leading role in the International Association for the Properties of Steam (IAPS, presently the International Association for the Properties of Water and
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.174 | 0.102 |
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".