Methodology for developing a neural network leaf spring model
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
This paper describes the development of a neural network that is able to emulate the vertical force-displacement behaviour of a leaf spring. Special emphasis is placed on aspects that affect the predictive capability of a neural network such as type, structure, inputs and ability to generalise. These aspects are investigated in order to enable the effective use of it to model leaf spring behaviour. The results show that with the correct selection of inputs and network architecture, the neural network's ability to generalise can be improved and also reduce the required training data. The resulting 2-15-1 feed-forward neural network is shown to generalise well and requires minimal data to be trained. Experimental data was used to train and validate the network. The methodology followed is not limited to the application of leaf springs only but should apply to various other applications especially ones with similar nonlinear characteristics.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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