Comparison of LoG (Laplacian of Gaussians) and DoG (Difference of Gaussians) Algorithms in the Measurement of the Nerve Quality Level of Gotu Gotu Leaves
Bibliographic record
Abstract
Gotu gotu leaves (Centella asiatica) are herbal plants with high pharmacological benefits thanks to the content of active compounds such as asiaticoside, madecassoside, and asiatic acid. The pharmacological quality of these leaves is greatly influenced by the condition of their neural networks. This study aims to compare two popular edge detection algorithms, namely Laplacian of Gaussian (LoG) and Difference of Gaussian (DoG), in measuring the level of nerve quality of the leaves of the peg. The method used is Python-based digital image processing with OpenCV, using primary data in the form of gotu tu leaf images in JPEG, PNG, and JPG formats. The results showed that the DoG algorithm was more efficient in computational time and was able to produce sharper leaf neural images, while LoG excelled at detecting the main line but tended to generate over-detection. Thus, the DoG is more suitable for the implementation of an automated system for assessing the quality of gotu gotu leaves in agriculture and pharmaceuticals.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".