Bibliographic record
Abstract
Chemistry is traditionally divided into a small number of subfields, namely organic, inorganic, analytical and physical chemistry. It's fairly easy to say what the first three are about, but it's much harder to define physical chemistry. The problem is that physical chemistry is all of the following simultaneously: A discipline in its own right, with its own set of problems and techniques; The source of the basic theory that underlies all of the chemical sciences; A provider of experimental methods used across the chemical sciences. Note that “chemical sciences” includes biochemistry and materials science, among other fields that depend on physical chemistry for at least some of their theory and methods. Physical chemistry's large mandate means that it's difficult to put a finger on what it is exactly. It's a bit like chemistry itself that way: every time you come up with a definition, you immediately think of half a dozen things done under that heading that don't fit. Rather than trying to give a simple, neat definition of physical chemistry, I'm going to tell you about the big theories that make up physical chemistry. Hopefully, this will give you an idea of what physical chemistry is about, even if we can't wrap it up in a neat package as we can with the other subfields of chemistry.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".