Bibliographic record
Abstract
Why Modify a Database? We have already mentioned that although most modeling programs come with an associated database, the responsibility for using appropriate data is always that of the user, not the programmer or agency which supplies the database. There are basically two situations which might call for the user to change or add to the data supplied with the program. One is, of course, if the data become obsolete; i.e., new or better data become available, superseding those already in the database. Because most users are not solution chemistry experts, and/or have no time to keep up with the latest developments in solution and surface chemistry, this may not be a common situation. It is not uncommon, however, to find that the database simply has no data for some element that becomes important to us. We must then obviously find some data, and add them to the database. The easiest way to do this is simply to take data from the database of some other modeling program that happens to be more complete with respect to the missing element or elements. Alternatively, we can consult the primary literature. In either case, it becomes important to understand the structure of the databases involved, because the modeling program expects to find the data in a certain format. To understand and conform to this format, we must read the program documentation, and in doing this it helps to understand the fundamentals of thermodynamics as given in Chapter 3.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".