Automated Neural-Network-Based Model Generation Algorithms for Microwave Applications
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
Automated Model Generation (AMG) algorithms are an important technique for creating Artificial Neural Network (ANN) models in microwave design automation. AMG integrates all the subtasks in ANN development into a unified automated algorithm. The assessments of the ANN training phenomena related to under-learning, over-learning and good-learning are automated and the quantitative links between the accuracy of the ANN model, the amount/distribution of training/testing data, and the size of the neural network are established. In this way, the AMG algorithm automatically creates an ANN model with user-desired accuracy, significantly reducing the human time required for modeling. This paper introduces the state of the art in AMG algorithms and its applications in microwave modeling.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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