Evaluation of photocatalytic activity of immobilized titania nanoparticles by support vector machine and artificial neural network
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 this study, TiO 2 nanoparticles immobilized on sackcloth fibre were used for the photodegradation of acid dye, and the efficiency of heterogeneous photocatalysts was predicted using the support vector machines model and artificial neural network model. Acid Red 73 was applied as a model compound. The experimental results were determined as the function of key factors such as initial H 2 O 2 concentration, dye concentration, dissolved anions, pH, and time. The obtained results were used for training the models. To find the most suitable and reliable network, different algorithms and transfer functions were tested. The trial and error method was used to find the optimum number of neurons and layers. The root mean squared of error (RMSE), the sum of square error (SSE), and R 2 for the models were calculated. Results show that support vector machines and neural network models can effectively learn and model the aforementioned process and predict the efficiency of photodegradation of coloured wastewater.
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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 | 0.000 |
| 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) | 0.000 | 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