Bibliographic record
Abstract
The aim of semantic science is to have scientific data and scientific theories in machine understandable form. Scientific theories make predictions on data. In the semantic science future, whenever someone does a scientific experiment, they publish the data using a formal ontology so that there is semantic interoperability; it can be compared with other data collected by others, and used to compare theories that make prediction on this data. When someone publishes a new theory, they publish it with respect to an ontology so they can test it on all available data about which it makes predictions. We could all see which theories predict the data better. By the use of formal ontologies, we could determine which are competing theories (when they make different predictions for the same data) and which are complementary. Whenever new data is collected, we can determine which theory better predicts the data. Human-made scientific theories can be compared with machine learned theories (of course, most theories are a mix). Imagine now the best theories applied to new cases: we can use the best medical theory to predict the disease a patient has, the best geological theory to predict where landslides will occur or the best economic theory to predict the effect of a policy change. This paper is preliminary and always under construction. If you have feedback, more references, please
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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.016 | 0.047 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".