Automated nanofibre sizing by multi‐image processing and deep learning with revised <scp>UNet</scp> 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
Abstract Nanofibres have been widely used in many chemical engineering applications and their performance greatly depends on the size distribution of the nanofibres. Researchers have developed automated tools to determine nanofibre diameters, primarily using commercial MATLAB software package. However, no researchers have reported automatic processing of multiple images, which is essential to the consistency and accuracy of results. Nor has anyone reported nanofibre sizing using deep learning. Therefore, this paper reports an automated tool to measure the size distribution of electrospun nanofibres by simultaneous multi‐image processing. This tool determines the diameters of nanofibres using deep learning based on UNet model. Results show that the UNet‐based deep learning approach is more accurate than those obtained using existing methods, compared to experimental data.
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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 | 0.000 |
| 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