Hybridizing UFO with Other ML Tools to Locate Faults by Just Knowing Relay Operating Times
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
Universal functions originator (UFO) is a new machine learning (ML) tool that can find relationships between responses and predictors and then automatically formulate them as mathematical equations using the required number of analytic functions and arithmetic operators. Since it was introduced in the literature there is still an urgent question about whether it is worthwhile to hybridize it with other ML tools, such as linear regression (LR), nonlinear regression (NLR), support vector machine (SVM), and artificial neural network (ANN). This study is the first attempt to hybridize UFO, as a universal transformation unit (UTU), with the preceding ML tools. The goal here is to let UTU take care of the non-linearity issue of the dataset before being sent to other ML tools. These new hybrid computing systems are applied to locate three-phase (3φ) faults in an electric power network by utilizing the operating times measured from the two-end numerical directional overcurrent relays (DOCRs) of a faulty line. The results show that the hybrid approaches are viable where their estimations are much better than those obtained by the classical ML tools. This study proves that the strong side of UFO can be integrated with others to have superior computing systems.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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