Non Invasive Decay Analysis of Monument Using Deep Learning Techniques
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
Monuments are a vital part of the heritage of any culture that witnesses the history of our time.These monuments are inherited from previous generations, and it is essential to preserve them through monitoring.The analysis of monument degradation employs a nondestructive method.One of the most effective Non-Destructive Techniques is Deep Learning Technique.The collected images from various monuments are preprocessed and fused with local binary pattern and Luminance.The integrated K-Means clustering used in the proposed approach automatically segments the moss and cracks in monuments, and multilayer neural networks are used to classify the decay.Performance parameters are assessed in terms of precision, accuracy, recall, and F1-Score after the decay parameters are identified using MLNN and validated.The novelty of the proposed work classifies the decay as moss or crack with an accuracy of approximately 97%.
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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