Polymer Color Properties: Neural Network Modeling
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
Abstract The prediction and optimization of polymer color properties is a complex problem with no easy method to predict color properties directly and accurately. The problem is especially complicated with the formulation of polymers to achieve the physical properties. By considering the formulation, the neural network has been implemented for prediction of color properties consisting of sigmoid hidden units and a linear output unit arranged in a feed forward back propagation architecture. An optimal design is accomplished for 15, 16, 17, 18, 19, and 20 hidden neurons with four different algorithms including gradient descent with momentum (GDM), resilient backpropagation (resilient backpropagation), scaled conjugate gradient (SCG), and Levenberg–Marquardt (LM) algorithm. The best result in terms of statistics is presented by the LM algorithm with 16 neurons in the designed artificial neural network (ANN) model. The degree of accuracy of the ANN model is proven acceptable in all statistical analysis and shown in results. However, it was concluded that ANN provides a possible method for the prediction of specific color tristimulus values.
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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 | 0.000 | 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