Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Theory Embedded Learning (TEL) combines theoretical principles with machine learning to solve Engineering problems. It offers a middle ground between principle-driven and learning-based approaches. Principle-driven approaches rely on assumptions to inject prior knowledge, to simplify the problem for analytical solvability, and the solution is represented by mathematical relations the solution should satisfy. Learning-based approaches, on the other hand, require minimal assumptions and use parameterized black-box functions without any prior knowledge. TEL modifies principle-driven approaches by retaining assumptions that represent prior knowledge while removing unnecessary assumptions for solvability. It achieves solvability through gradient based optimization, representing the solution with parameterized mathematical relationships. Therefore, TEL benefits from the flexibility of learning-based approaches and the rigidity of principle-driven approaches, without any unnecessary assumptions made for solvability. This work identifies TEL solution approach as well as introduces three original instances of TEL for solving problems in optimal control, derivative pricing and combinatorial optimization.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it