Bibliographic record
Abstract
Introduction We have had quite enough theoretical discussion for now. Let's see how to get some numbers into our equations so as to be able to calculate something useful. Welcome to the world of experimental thermochemistry. In this chapter we will have a look at a few of the ways in which the thermodynamic parameters we have derived are measured; i.e., where the numbers in the tables and databases come from. A deep knowledge of this subject is not necessary in order to use thermodynamics to model chemical, geological, or environmental systems, in the same sense that a knowledge of a composer's life and times is not necessary to enjoy his or her music. But it does enrich the experience, and in the case of using thermodynamics, such knowledge does serve to make the user conscious of the many, many reasons why his or her data might be incorrect. It enables the user to truly believe in the “modeler's motto” – never trust your data absolutely. Thermochemical data are produced for the most part by dedicated scientists, who devote a good part of their lives to tracking down elusive sources of error, and devising ever-improved methods for determining nature's fundamental parameters as defined by thermodynamic theory. When determined by independent methods and/or independent laboratories, the results are often satisfyingly in agreement, but almost as often they are not, meaning that there is some source of error, and identifying it can take a lot of discussion (perhaps arguments would be a better term) and a long time.
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 | 0.019 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.408 | 0.394 |
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".