Universal Functions Originator—Part II: Evaluation
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
In the first part of this study, a brief overview about the basic design of the universal functions originator (UFO) is introduced without conducting any numerical experiment to evaluate its performance and explore its capabilities. This part of the study is allocated to cover the practical side of the proposed new AI computing system (patent-pending). For this mission, a highly advanced graphical user interface (GUI) has been designed with multiple options to see how UFO can perform against different data size problems. The current version of the GUI is based on the one output stream structure presented in the first part of the study. Two “function approximation” problems are given in this paper. After being solved by UFO, they are also solved by regression analysis, support vector machines (SVMs), and artificial neural networks (ANNs); using different approaches and configurations. Based on the results obtained from these experiments, there is an obvious evidence that UFO is a very competitive computing system; and thus it could open new promising research areas.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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